From babb3e187680291febc010d17f283ace81b5f01f Mon Sep 17 00:00:00 2001 From: a2rlab2 Date: Mon, 10 Jun 2024 17:48:29 -0400 Subject: [PATCH] removed stable-baselines dependency --- CACTO DEPENDENCY DEBUGGING READ ME | 146 ++++ .../Code/N_try_0/NeuralNetwork.py | 233 +++++ .../Results set test/Code/N_try_0/RL.py | 233 +++++ .../Results set test/Code/N_try_0/TO.py | 202 +++++ .../Results set test/Code/N_try_0/conf_car.py | 203 +++++ .../Code/N_try_0/conf_car_park.py | 221 +++++ .../Code/N_try_0/conf_double_integrator.py | 213 +++++ .../Code/N_try_0/conf_manipulator.py | 225 +++++ .../Code/N_try_0/conf_single_integrator.py | 200 +++++ .../Results set test/Code/N_try_0/conf_ur5.py | 227 +++++ .../Code/N_try_0/environment.py | 816 ++++++++++++++++++ .../Code/N_try_0/environment_TO.py | 778 +++++++++++++++++ .../Code/N_try_0/generate_tests_set_script.py | 26 + .../Results set test/Code/N_try_0/main.py | 254 ++++++ .../Code/N_try_0/plot_utils.py | 714 +++++++++++++++ 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mode 100644 __pycache__/plot_utils.cpython-310.pyc create mode 100644 __pycache__/replay_buffer.cpython-310.pyc create mode 100644 __pycache__/robot_utils.cpython-310.pyc create mode 100644 __pycache__/segment_tree.cpython-310.pyc create mode 100644 __pycache__/utils.cpython-310.pyc create mode 100644 requirements.txt create mode 100644 segment_tree.py diff --git a/CACTO DEPENDENCY DEBUGGING READ ME b/CACTO DEPENDENCY DEBUGGING READ ME new file mode 100644 index 0000000..a32ac95 --- /dev/null +++ b/CACTO DEPENDENCY DEBUGGING READ ME @@ -0,0 +1,146 @@ +#This is a helper document I'm writing to assist in debugging dependency installations for the cacto-sl code. +#Nadim Kanazi +#June 2024 +________________________________________________________________________________________________ + +The general approach was this for starters: + +1. Clone the cacto repo (https://github.com/gianluigigrandesso/cacto) +2. Follow the instructions below to run the code. These instructions were provided by ELisa Alboni: + - create and activate a new virtual environment (python3.10.12) via conda + - python3 -m pip install --upgrade pip + - python3 -m pip install tensorflow==2.11 + - git clone https://github.com/nmansard/jnrh2023 + - cd jnrh2023 + - python3 -m pip install example-robot-data-jnrh2023 jupyterlab meshcat scipy ipywidgets matplotlib + - python3 -m pip install tf_siren + +Try running a test after these installs to check if the environment is working: + +python3 main.py --system-id='single_integrator' --seed=0 --nb-cpus=15 --w-S=1e-2 --test-n=0 +________________________________________________________________________________________________ + +#Issues I ran into: + + 1- Python/tensorflow versions, pygame, and stable-baselines + + These are a bunch of closely related issues with versions of python and of these packages. The best I can explain it is as follows: + +When first running the tests directly after following the instructions above, this is the first error: + - File "/home/a2rlab2/.local/lib/python3.10/site-packages/stable_baselines/deepq/policies.py", line 2, in + import tensorflow.contrib.layers as tf_layers +ModuleNotFoundError: No module named 'tensorflow.contrib' + +The stable-baselines package attempted to access the tensorflow.contrib module, but this module was removed from tensorflow 2.X and is only present in 1.X versions. So naturally an easy solution is to downgrade tensorflow to an earlier version. This is corroborated by the stable-baselines documentation, which states that stable-baselines does not support tensorflow 2.X and is only compatible with tensorflow 1.8 to 1.13: + https://stable-baselines.readthedocs.io/en/master/guide/install.html + + The next issue when trying to downgrade is that tensorflow 1.X does not exist in python 3.8 or later. Python 3.7 or earlier is needed for this. I tried to create a new conda environment with python 3.7 and follow the instructions again, but then the following command does not work: + +python3 -m pip install example-robot-data-jnrh2023 jupyterlab meshcat scipy ipywidgets matplotlib + +so I went directly to the jnrh2023 repo and followed their instructions, which require pinocchio 2.99. But pinocchio 2.99 is only available in python >= 3.8, so this entire approach would not work at all. + + + 2- Pinocchio and Casadi interface issues: + + Error: + ModuleNotFoundError: No module named 'pinocchio.casadi' + +Explanation: It seems only pinocchio 3 is compatible with casadi as earlier versions are not. Pinocchio 3 can be built from this branch: +https://github.com/stack-of-tasks/pinocchio/tree/pinocchio3-preview + +I believe Pinnochio3 is not available to install using pip or conda. Here are a few helpful links: + +https://gitlab.cc-asp.fraunhofer.de/hanikevi/contact_mpc +https://github.com/nmansard/jnrh2023 +https://github.com/ami-iit/ami-commons/blob/master/doc/casadi-ipopt-hsl.md + +I tried building directly from the branch but got a lot of memory-related errors. + + 3- requirements.txt file: + + ERROR: Could not find a version that satisfies the requirement [some package] (from versions: none) +ERROR: No matching distribution found for [some package] + +I don't understand this error and what the packages listed in requirements.txt are really. I tried looking up some of them but haven't found any info about some (ex: ament-cmake, section-pkgs, action-msgs). I tried installing them with pip and coda but no luck in either. They may be ROS packages, here is the info I found online: +https://index.ros.org/search/?term=ament-cmake-test§ion=pkgs +https://index.ros.org/search/?term=action-msgs§ion=pkgs + +Note: this command is helpful to prevent pip from failing on a single line of requirements.txt + +cat requirements.txt | xargs -n 1 pip install + + +4- Other misc errors: protobuf, pygame, wheel: + +Unfortunately (or fortunately), I haven't been able to replicate these errors since last week but I was having some trouble at one point with these packages. I don't recall when or how though. Please ignore the call stack below as it is only for my personal debugging use + +Building wheels for collected packages: pygame + Building wheel for pygame (setup.py) ... error + error: subprocess-exited-with-error + + × python setup.py bdist_wheel did not run successfully. + │ exit code: 1 + ╰─> [44 lines of output] + src_c/imageext.c: In function ‘image_load_ext’: + src_c/imageext.c:118:13: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] + 118 | ext = find_extension(name); + | ^ + src_c/imageext.c:107:9: warning: variable ‘lock_mutex’ set but not used [-Wunused-but-set-variable] + 107 | int lock_mutex = 0; + | ^~~~~~~~~~ + src_c/font.c: In function ‘font_dealloc’: + src_c/font.c:693:29: warning: initialization of ‘int **’ from incompatible pointer type ‘TTF_Font *’ {aka ‘struct _TTF_Font *’} [-Wincompatible-pointer-types] + 693 | int** face_pp = font; + | ^~~~ + At top level: + src_c/font.c:78:1: warning: ‘utf_8_needs_UCS_4’ defined but not used [-Wunused-function] + 78 | utf_8_needs_UCS_4(const char *str) + | ^~~~~~~~~~~~~~~~~ + src_c/music.c: In function ‘_get_type_from_hint’: + src_c/music.c:307:22: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] + 307 | namehint = dot + 1; + | ^ + In file included from src_c/pygame.h:30, + from src_c/music.c:27: + src_c/music.c: In function ‘_load_music’: + src_c/_pygame.h:253:47: warning: returning ‘PyObject *’ {aka ‘struct _object *’} from a function with incompatible return type ‘Mix_Music *’ {aka ‘struct _Mix_Music *’} [-Wincompatible-pointer-types] + 253 | #define RAISE(x, y) (PyErr_SetString((x), (y)), (PyObject *)NULL) + | ~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~ + src_c/mixer.h:9:16: note: in expansion of macro ‘RAISE’ + 9 | return RAISE(pgExc_SDLError, "mixer not initialized") + | ^~~~~ + src_c/music.c:377:5: note: in expansion of macro ‘MIXER_INIT_CHECK’ + 377 | MIXER_INIT_CHECK(); + | ^~~~~~~~~~~~~~~~ + src_c/pypm.c:209:12: fatal error: longintrepr.h: No such file or directory + 209 | #include "longintrepr.h" + | ^~~~~~~~~~~~~~~ + compilation terminated. + + --- + For help with compilation see: + https://www.pygame.org/wiki/Compilation + To contribute to pygame development see: + https://www.pygame.org/contribute.html + --- + + error: command '/usr/bin/gcc' failed with exit code 1 + [end of output] + + note: This error originates from a subprocess, and is likely not a problem with pip. + ERROR: Failed building wheel for pygame + Running setup.py clean for pygame +Failed to build pygame +ERROR: Could not build wheels for pygame, which is required to install pyproject.toml-based projects +(cacto-env) a2rlab2@a2rlab-lenovo-1:~$ sudo apt-get install python-edv + +________________________________________________________________________________________________ + + +Solved by downloading segment_tree from openai baselines file and changing import line within replay_buffer.py + + + + + diff --git a/Results Double Integrator/Results set test/Code/N_try_0/NeuralNetwork.py b/Results Double Integrator/Results set test/Code/N_try_0/NeuralNetwork.py new file mode 100644 index 0000000..90f4127 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/NeuralNetwork.py @@ -0,0 +1,233 @@ +import math +import random +import numpy as np +import tensorflow as tf +from tensorflow.keras import layers, regularizers +from tf_siren import SinusodialRepresentationDense + +from utils import normalize_tensor + +class NN: + + def __init__(self, env, conf, w_S=0): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :param NH1: (int) 1st hidden layer size + :param NH2: (int) 2nd hidden layer size + :param kreg_l1_A : (float) Weight of L1 regularization in actor's network - kernel + :param kreg_l2_A : (float) Weight of L2 regularization in actor's network - kernel + :param breg_l1_A : (float) Weight of L2 regularization in actor's network - bias + :param breg_l2_A : (float) Weight of L2 regularization in actor's network - bias + :param kreg_l1_C : (float) Weight of L1 regularization in critic's network - kernel + :param kreg_l2_C : (float) Weight of L2 regularization in critic's network - kernel + :param breg_l1_C : (float) Weight of L1 regularization in critic's network - bias + :param breg_l2_C : (float) Weight of L2 regularization in critic's network - bias + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param NORMALIZE_INPUTS : (bool) Flag to normalize inputs (state) + :param state_norm_array : (float array) Array used to normalize states + :param MC : (bool) Flag to use MC or TD(n) + :param cost_weights_terminal : (float array) Running cost weights vector + :param cost_weights_running : (float array) Terminal cost weights vector + :param BATCH_SIZE : (int) Size of the mini-batch + :param dt : (float) Timestep + + :input w_S : (float) Sobolev-training weight + ''' + + self.env = env + self.conf = conf + + self.w_S = w_S + + self.MSE = tf.keras.losses.MeanSquaredError() + + return + + def create_actor(self): + ''' Create actor NN ''' + inputs = layers.Input(shape=(self.conf.nb_state,)) + + lay1 = layers.Dense(self.conf.NH1,kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(inputs) + leakyrelu1 = layers.LeakyReLU()(lay1) + lay2 = layers.Dense(self.conf.NH2, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(leakyrelu1) + leakyrelu2 = layers.LeakyReLU()(lay2) + outputs = layers.Dense(self.conf.nb_action, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(leakyrelu2) + + model = tf.keras.Model(inputs, outputs) + + return model + + def create_critic_elu(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = layers.Dense(16, activation='elu')(state_input) + state_out2 = layers.Dense(32, activation='elu')(state_out1) + out_lay1 = layers.Dense(256, activation='elu')(state_out2) + out_lay2 = layers.Dense(256, activation='elu')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_sine_elu(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = SinusodialRepresentationDense(64, activation='sine')(state_input) + state_out2 = layers.Dense(64, activation='elu')(state_out1) + out_lay1 = SinusodialRepresentationDense(128, activation='sine')(state_out2) + out_lay2 = layers.Dense(128, activation='elu')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_sine(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = SinusodialRepresentationDense(64, activation='sine')(state_input) + state_out2 = SinusodialRepresentationDense(64, activation='sine')(state_out1) + out_lay1 = SinusodialRepresentationDense(128, activation='sine')(state_out2) + out_lay2 = SinusodialRepresentationDense(128, activation='sine')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_relu(self): + ''' Create critic NN - relu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = layers.Dense(16, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(state_input) + leakyrelu1 = layers.LeakyReLU()(state_out1) + + state_out2 = layers.Dense(32, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu1) + leakyrelu2 = layers.LeakyReLU()(state_out2) + out_lay1 = layers.Dense(self.conf.NH1, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu2) + leakyrelu3 = layers.LeakyReLU()(out_lay1) + out_lay2 = layers.Dense(self.conf.NH2, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu3) + leakyrelu4 = layers.LeakyReLU()(out_lay2) + + outputs = layers.Dense(1, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu4) + + model = tf.keras.Model([state_input], outputs) + + return model + + def eval(self, NN, input): + ''' Compute the output of a NN given an input ''' + if not tf.is_tensor(input): + input = tf.convert_to_tensor(input, dtype=tf.float32) + + if self.conf.NORMALIZE_INPUTS: + input = normalize_tensor(input, self.conf.state_norm_arr) + + return NN(input, training=True) + + def custom_logarithm(self,input): + # Calculate the logarithms based on the non-zero condition + positive_log = tf.math.log(tf.math.maximum(input, 1e-7) + 1) + negative_log = -tf.math.log(tf.math.maximum(-input, 1e-7) + 1) + + # Use the appropriate logarithm based on the condition + result = tf.where(input > 0, positive_log, negative_log) + + return result + + def compute_critic_grad(self, critic_model, target_critic, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, weights_batch): + ''' Compute the gradient of the critic NN ''' + with tf.GradientTape() as tape: + # Compute value function tail if TD(n) is used + if self.conf.MC: + reward_to_go_batch = partial_reward_to_go_batch + else: + target_values = self.eval(target_critic, state_next_rollout_batch) # Compute Value at next state after conf.nsteps_TD_N steps given by target critic + reward_to_go_batch = partial_reward_to_go_batch + (1-d_batch)*target_values # Compute batch of 1-step targets for the critic loss + + # Compute critic loss + if self.w_S != 0: + with tf.GradientTape() as tape2: + tape2.watch(state_batch) + critic_value = self.eval(critic_model, state_batch) + der_critic_value = tape2.gradient(critic_value, state_batch) + + critic_loss_v = self.MSE(reward_to_go_batch, critic_value, sample_weight=weights_batch) + critic_loss_der = self.MSE(self.custom_logarithm(dVdx_batch[:,:-1]), self.custom_logarithm(der_critic_value[:,:-1]), sample_weight=weights_batch) # dV/dt not computed and so not used in the update + + critic_loss = critic_loss_der + self.w_S*critic_loss_v + else: + critic_value = self.eval(critic_model, state_batch) + critic_loss = self.MSE(reward_to_go_batch, critic_value, sample_weight=weights_batch) + + # Compute the gradients of the critic loss w.r.t. critic's parameters + critic_grad = tape.gradient(critic_loss, critic_model.trainable_variables) + + return critic_grad, reward_to_go_batch, critic_value, self.eval(target_critic, state_batch) + + def compute_actor_grad(self, actor_model, critic_model, state_batch, term_batch, batch_size): + ''' Compute the gradient of the actor NN ''' + if batch_size == None: + batch_size = self.conf.BATCH_SIZE + + actions = self.eval(actor_model, state_batch) + + # Both take into account normalization, ds_next_da is the gradient of the dynamics w.r.t. policy actions (ds'_da) + state_next_tf, ds_next_da = self.env.simulate_batch(state_batch.numpy(), actions.numpy()) , self.env.derivative_batch(state_batch.numpy(), actions.numpy()) + + with tf.GradientTape() as tape: + tape.watch(state_next_tf) + critic_value_next = self.eval(critic_model,state_next_tf) + + # dV_ds' = gradient of V w.r.t. s', where s'=f(s,a) a=policy(s) + dV_ds_next = tape.gradient(critic_value_next, state_next_tf) + + cost_weights_terminal_reshaped = np.reshape(self.conf.cost_weights_terminal,[1,len(self.conf.cost_weights_terminal)]) + cost_weights_running_reshaped = np.reshape(self.conf.cost_weights_running,[1,len(self.conf.cost_weights_running)]) + with tf.GradientTape() as tape1: + tape1.watch(actions) + rewards_tf = self.env.reward_batch(term_batch.dot(cost_weights_terminal_reshaped) + (1-term_batch).dot(cost_weights_running_reshaped), state_batch.numpy(), actions) + + # dr_da = gradient of reward r(s,a) w.r.t. policy's action a + dr_da = tape1.gradient(rewards_tf, actions, unconnected_gradients=tf.UnconnectedGradients.ZERO) + + dr_da_reshaped = tf.reshape(dr_da, (batch_size, 1, self.conf.nb_action)) + + # dr_ds' + dV_ds' (note: dr_ds' = 0) + dQ_ds_next = tf.reshape(dV_ds_next, (batch_size, 1, self.conf.nb_state)) + + # (dr_ds' + dV_ds')*ds'_da + dQ_ds_next_da = tf.matmul(dQ_ds_next, ds_next_da) + + # (dr_ds' + dV_ds')*ds'_da + dr_da + dQ_da = dQ_ds_next_da + dr_da_reshaped + + # Now let's multiply -[(dr_ds' + dV_ds')*ds'_da + dr_da] by the actions a + # and then let's autodifferentiate w.r.t theta_A (actor NN's parameters) to finally get -dQ/dtheta_A + with tf.GradientTape() as tape: + tape.watch(actor_model.trainable_variables) + actions = self.eval(actor_model, state_batch) + + actions_reshaped = tf.reshape(actions,(batch_size,self.conf.nb_action,1)) + dQ_da_reshaped = tf.reshape(dQ_da,(batch_size,1,self.conf.nb_action)) + Q_neg = tf.matmul(-dQ_da_reshaped,actions_reshaped) + + # Also here we need a scalar so we compute the mean -Q across the batch + mean_Qneg = tf.math.reduce_mean(Q_neg) + + # Gradients of the actor loss w.r.t. actor's parameters + actor_grad = tape.gradient(mean_Qneg, actor_model.trainable_variables) + + return actor_grad \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_0/RL.py b/Results Double Integrator/Results set test/Code/N_try_0/RL.py new file mode 100644 index 0000000..fa60798 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/RL.py @@ -0,0 +1,233 @@ +import uuid +import math +import numpy as np +import tensorflow as tf + +class RL_AC: + def __init__(self, env, NN, conf, N_try): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :parma critic_type : (str) Activation function to use for the critic NN + :param LR_SCHEDULE : (bool) Flag to use a scheduler for the learning rates + :param boundaries_schedule_LR_C : (list) Boudaries of critic LR + :param values_schedule_LR_C : (list) Values of critic LR + :param boundaries_schedule_LR_A : (list) Boudaries of actor LR + :param values_schedule_LR_A : (list) Values of actor LR + :param CRITIC_LEARNING_RATE : (float) Learning rate for the critic network + :param ACTOR_LEARNING_RATE : (float) Learning rate for the policy network + :param fresh_factor : (float) Refresh factor + :param prioritized_replay_alpha : (float) α determines how much prioritization is used + :param prioritized_replay_eps : (float) It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero + :param UPDATE_LOOPS : (int array) Number of updates of both critic and actor performed every EP_UPDATE episodes + :param save_interval : (int) save NNs interval + :param env_RL : (bool) Flag RL environment + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param MC : (bool) Flag to use MC or TD(n) + :param nsteps_TD_N : (int) Number of lookahed steps if TD(n) is used + :param UPDATE_RATE : (float) Homotopy rate to update the target critic network if TD(n) is used + :param cost_weights_terminal : (float array) Running cost weights vector + :param cost_weights_running : (float array) Terminal cost weights vector + :param dt : (float) Timestep + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param NNs_path : (str) NNs path + :param NSTEPS : (int) Max episode length + + ''' + self.env = env + self.NN = NN + self.conf = conf + + self.N_try = N_try + + self.actor_model = None + self.critic_model = None + self.target_critic = None + self.actor_optimizer = None + self.critic_optimizer = None + + self.init_rand_state = None + self.NSTEPS_SH = 0 + self.control_arr = None + self.state_arr = None + self.ee_pos_arr = None + self.exp_counter = np.zeros(conf.REPLAY_SIZE) + + return + + def setup_model(self, recover_training=None): + ''' Setup RL model ''' + # Create actor, critic and target NNs + self.actor_model = self.NN.create_actor() + + if self.conf.critic_type == 'elu': + self.critic_model = self.NN.create_critic_elu() + self.target_critic = self.NN.create_critic_elu() + elif self.conf.critic_type == 'sine': + self.critic_model = self.NN.create_critic_sine() + self.target_critic = self.NN.create_critic_sine() + elif self.conf.critic_type == 'sine-elu': + self.critic_model = self.NN.create_critic_sine_elu() + self.target_critic = self.NN.create_critic_sine_elu() + else: + self.critic_model = self.NN.create_critic_relu() + self.target_critic = self.NN.create_critic_relu() + + # Set optimizer specifying the learning rates + if self.conf.LR_SCHEDULE: + # Piecewise constant decay schedule + self.CRITIC_LR_SCHEDULE = tf.keras.optimizers.schedules.PiecewiseConstantDecay(self.conf.boundaries_schedule_LR_C, self.conf.values_schedule_LR_C) + self.ACTOR_LR_SCHEDULE = tf.keras.optimizers.schedules.PiecewiseConstantDecay(self.conf.boundaries_schedule_LR_A, self.conf.values_schedule_LR_A) + self.critic_optimizer = tf.keras.optimizers.Adam(self.CRITIC_LR_SCHEDULE) + self.actor_optimizer = tf.keras.optimizers.Adam(self.ACTOR_LR_SCHEDULE) + else: + self.critic_optimizer = tf.keras.optimizers.Adam(self.conf.CRITIC_LEARNING_RATE) + self.actor_optimizer = tf.keras.optimizers.Adam(self.conf.ACTOR_LEARNING_RATE) + + # Set initial weights of the NNs + if recover_training is not None: + NNs_path_rec = str(recover_training[0]) + N_try = recover_training[1] + update_step_counter = recover_training[2] + self.actor_model.load_weights("{}/N_try_{}/actor_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + self.critic_model.load_weights("{}/N_try_{}/critic_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + self.target_critic.load_weights("{}/N_try_{}/target_critic_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + else: + self.target_critic.set_weights(self.critic_model.get_weights()) + + def update(self, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, term_batch, weights_batch, batch_size=None): + ''' Update both critic and actor ''' + # Update the critic backpropagating the gradients + critic_grad, reward_to_go_batch, critic_value, target_critic_value = self.NN.compute_critic_grad(self.critic_model, self.target_critic, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, weights_batch) + self.critic_optimizer.apply_gradients(zip(critic_grad, self.critic_model.trainable_variables)) + + # Update the actor backpropagating the gradients + actor_grad = self.NN.compute_actor_grad(self.actor_model, self.critic_model, state_batch, term_batch, batch_size) + self.actor_optimizer.apply_gradients(zip(actor_grad, self.actor_model.trainable_variables)) + + return reward_to_go_batch, critic_value, target_critic_value + + @tf.function + def update_target(self, target_weights, weights): + ''' Update target critic NN ''' + tau = self.conf.UPDATE_RATE + for (a, b) in zip(target_weights, weights): + a.assign(b * tau + a * (1 - tau)) + + def learn_and_update(self, update_step_counter, buffer, ep): + ''' Sample experience and update buffer priorities and NNs ''' + for i in range(int(self.conf.UPDATE_LOOPS[ep])): + # Sample batch of transitions from the buffer + state_batch, partial_reward_to_go_batch, state_next_rollout_batch, dVdx_batch, d_batch, term_batch, weights_batch, batch_idxes = buffer.sample() + + # Update both critic and actor + reward_to_go_batch, critic_value, target_critic_value = self.update(state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, term_batch, weights_batch) + + # Update buffer priorities + if self.conf.prioritized_replay_alpha != 0: + buffer.update_priorities(batch_idxes, reward_to_go_batch, critic_value, target_critic_value) + + # Update target critic + if not self.conf.MC: + self.update_target(self.target_critic.variables, self.critic_model.variables) + + update_step_counter += 1 + + # Plot rollouts and save the NNs every conf.log_rollout_interval-training episodes + if update_step_counter%self.conf.save_interval == 0: + self.RL_save_weights(update_step_counter) + + return update_step_counter + + def RL_Solve(self, TO_controls, TO_states, TO_step_cost): + ''' Solve RL problem ''' + ep_return = 0 # Initialize the return + rwrd_arr = np.empty(self.NSTEPS_SH+1) # Reward array + state_next_rollout_arr = np.zeros((self.NSTEPS_SH+1, self.conf.nb_state)) # Next state array + partial_reward_to_go_arr = np.empty(self.NSTEPS_SH+1) # Partial cost-to-go array + total_reward_to_go_arr = np.empty(self.NSTEPS_SH+1) # Total cost-to-go array + term_arr = np.zeros(self.NSTEPS_SH+1) # Episode-termination flag array + term_arr[-1] = 1 + done_arr = np.zeros(self.NSTEPS_SH+1) # Episode-MC-termination flag array + + # START RL EPISODE + self.control_arr = TO_controls # action clipped in TO + + if self.conf.env_RL: + for step_counter in range(self.NSTEPS_SH): + # Simulate actions and retrieve next state and compute reward + self.state_arr[step_counter+1,:], rwrd_arr[step_counter] = self.env.step(self.conf.cost_weights_running, self.state_arr[step_counter,:], self.control_arr[step_counter,:]) + + # Compute end-effector position + self.ee_pos_arr[step_counter+1,:] = self.env.get_end_effector_position(self.state_arr[step_counter+1, :]) + rwrd_arr[-1] = self.env.reward(self.conf.cost_weights_terminal, self.state_arr[-1,:]) + else: + self.state_arr, rwrd_arr = TO_states, -TO_step_cost + + ep_return = sum(rwrd_arr) + + # Store transition after computing the (partial) cost-to go when using n-step TD (from 0 to Monte Carlo) + for i in range(self.NSTEPS_SH+1): + # set final lookahead step depending on whether Monte Cartlo or TD(n) is used + if self.conf.MC: + final_lookahead_step = self.NSTEPS_SH + done_arr[i] = 1 + else: + final_lookahead_step = min(i+self.conf.nsteps_TD_N, self.NSTEPS_SH) + if final_lookahead_step == self.NSTEPS_SH: + done_arr[i] = 1 + else: + state_next_rollout_arr[i,:] = self.state_arr[final_lookahead_step+1,:] + + # Compute the partial and total cost to go + partial_reward_to_go_arr[i] = np.float32(sum(rwrd_arr[i:final_lookahead_step+1])) + total_reward_to_go_arr[i] = np.float32(sum(rwrd_arr[i:self.NSTEPS_SH+1])) + + return self.state_arr, partial_reward_to_go_arr, total_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, self.ee_pos_arr + + def RL_save_weights(self, update_step_counter='final'): + ''' Save NN weights ''' + self.actor_model.save_weights(self.conf.NNs_path+"/N_try_{}/actor_{}.h5".format(self.N_try,update_step_counter)) + self.critic_model.save_weights(self.conf.NNs_path+"/N_try_{}/critic_{}.h5".format(self.N_try,update_step_counter)) + self.target_critic.save_weights(self.conf.NNs_path+"/N_try_{}/target_critic_{}.h5".format(self.N_try,update_step_counter)) + + def create_TO_init(self, ep, ICS): + ''' Create initial state and initial controls for TO ''' + self.init_rand_state = ICS + + self.NSTEPS_SH = self.conf.NSTEPS - int(self.init_rand_state[-1]/self.conf.dt) + if self.NSTEPS_SH == 0: + return None, None, None, None, 0 + + # Initialize array to store RL state, control, and end-effector trajectories + self.control_arr = np.empty((self.NSTEPS_SH, self.conf.nb_action)) + self.state_arr = np.empty((self.NSTEPS_SH+1, self.conf.nb_state)) + self.ee_pos_arr = np.empty((self.NSTEPS_SH+1,3)) + + # Set initial state and end-effector position + self.state_arr[0,:] = self.init_rand_state + self.ee_pos_arr[0,:] = self.env.get_end_effector_position(self.state_arr[0, :]) + + # Initialize array to initialize TO state and control variables + init_TO_controls = np.zeros((self.NSTEPS_SH, self.conf.nb_action)) + init_TO_states = np.zeros(( self.NSTEPS_SH+1, self.conf.nb_state)) + + # Set initial state + init_TO_states[0,:] = self.init_rand_state + + # Simulate actor's actions to compute the state trajectory used to initialize TO state variables (use ICS for state and 0 for control if it is the first episode otherwise use policy rollout) + success_init_flag = 1 + for i in range(self.NSTEPS_SH): + if ep == 0: + init_TO_controls[i,:] = np.zeros(self.conf.nb_action) + else: + init_TO_controls[i,:] = tf.squeeze(self.NN.eval(self.actor_model, np.array([init_TO_states[i,:]]))).numpy() + init_TO_states[i+1,:] = self.env.simulate(init_TO_states[i,:],init_TO_controls[i,:]) + if np.isnan(init_TO_states[i+1,:]).any(): + success_init_flag = 0 + return None, None, None, None, success_init_flag + + return self.init_rand_state, init_TO_states, init_TO_controls, self.NSTEPS_SH, success_init_flag \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_0/TO.py b/Results Double Integrator/Results set test/Code/N_try_0/TO.py new file mode 100644 index 0000000..2468938 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/TO.py @@ -0,0 +1,202 @@ +import sys +import math +import casadi +import numpy as np +import pinocchio.casadi as cpin + +class TO_Casadi: + + def __init__(self, env, conf, env_TO, w_S=0): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param u_min : (float array) Action lower bound array + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param dt : (float) Timestep + + :input system_id : (str) Id system + + :input w_S : (float) Sobolev-training weight + ''' + + self.env = env + self.conf = conf + + self.nx = conf.nx + self.nu = conf.na + + self.w_S = w_S + + self.CAMS = env_TO + + def TO_System_Solve(self, ICS_state, init_TO_states, init_TO_controls, T): + ''' Create and solbe TO casadi problem ''' + ### PROBLEM + opti = casadi.Opti() + + # The control models are stored as a collection of shooting nodes called running models, with an additional terminal model. + self.runningSingleModel = self.CAMS('running_model', self.conf) + runningModels = [ self.runningSingleModel for _ in range(T) ] + self.terminalModel = self.CAMS('terminal_model', self.conf) + + # Decision variables + xs = [ opti.variable(model.nx) for model in runningModels+[self.terminalModel] ] # state variable + us = [ opti.variable(model.nu) for model in runningModels ] # control variable + + # Roll out loop, summing the integral cost and defining the shooting constraints. + total_cost = 0 + + opti.subject_to(xs[0] == ICS_state[:-1]) + + for t in range(T): + x_next, r_cost = runningModels[t].step_fun(xs[t], us[t]) + opti.subject_to(xs[t + 1] == x_next ) + total_cost += r_cost + r_cost_final = self.terminalModel.cost(xs[-1], us[-1]) + total_cost += r_cost_final + + ### SOLVE + opti.minimize(total_cost) + + # Create warmstart + init_x_TO = [np.array(init_TO_states[i,:-1]) for i in range(T+1)] + init_u_TO = [np.array(init_TO_controls[i,:]) for i in range(T)] + + for x,xg in zip(xs,init_x_TO): opti.set_initial(x,xg) + for u,ug in zip(us,init_u_TO): opti.set_initial(u,ug) + + # Set solver options + #opts = {'ipopt.linear_solver':'ma57', 'ipopt.sb': 'yes','ipopt.print_level': 0, 'print_time': 0} #, 'ipopt.max_iter': 500} + opts = {'ipopt.sb': 'yes','ipopt.print_level': 0, 'print_time': 0} #, 'ipopt.max_iter': 500} + opti.solver("ipopt", opts) + + try: + opti.solve() + TO_states = np.array([ opti.value(x) for x in xs ]) + TO_controls = np.array([ opti.value(u) for u in us ]) + TO_total_cost = opti.value(total_cost) + TO_ee_pos_arr = np.empty((T+1,3)) + TO_step_cost = np.empty(T+1) + for n in range(T): + TO_ee_pos_arr[n,:] = np.reshape(runningModels[n].p_ee(TO_states[n,:]),-1) + TO_step_cost[n] = runningModels[n].cost(TO_states[n,:], TO_controls[n,:]) + TO_ee_pos_arr[-1,:] = np.reshape(self.terminalModel.p_ee(TO_states[-1,:]),-1) + TO_step_cost[-1] = self.terminalModel.cost(TO_states[-1,:], TO_controls[-1,:]) + success_flag = 1 + except: + print('ERROR in convergence, returning debug values') + TO_states = np.array([ opti.debug.value(x) for x in xs ]) + TO_controls = np.array([ opti.debug.value(u) for u in us ]) + TO_total_cost = None + TO_ee_pos_arr = None + TO_step_cost = None + success_flag = 0 + + return success_flag, TO_controls, TO_states, TO_ee_pos_arr, TO_total_cost, TO_step_cost + + def TO_Solve(self, ICS_state, init_TO_states, init_TO_controls, T): + ''' Retrieve TO problem solution and compute the value function derviative with respect to the state ''' + success_flag, TO_controls, TO_states, TO_ee_pos_arr, _, TO_step_cost = self.TO_System_Solve(ICS_state, init_TO_states, init_TO_controls, T) + if success_flag == 0: + return None, None, success_flag, None, None, None + + if self.w_S != 0: + # Compute V gradient w.r.t. x (no computation dV/dt) + dVdx = self.backward_pass(T+1, TO_states, TO_controls) + else: + dVdx = np.zeros((T+1, self.conf.nb_state)) + + # Add the last state component (time) + TO_states = np.concatenate((TO_states, init_TO_states[0,-1] + np.transpose(self.conf.dt*np.array([range(T+1)]))), axis=1) + + return TO_controls, TO_states, success_flag, TO_ee_pos_arr, TO_step_cost, dVdx + + def backward_pass(self, T, TO_states, TO_controls, mu=1e-9): + ''' Perform the backward-pass of DDP to obtain the derivatives of the Value function w.r.t x ''' + n = self.conf.nb_state-1 + m = self.conf.nb_action + + X_bar = np.zeros((T, n)) + for i in range(n): + X_bar[:,i] = [TO_states[t,i] for t in range(T)] + + U_bar = np.zeros((T-1, m)) + for i in range(m): + U_bar[:,i] = [TO_controls[t,i] for t in range(T-1)] + + # The task is defined by a quadratic cost: + # sum_{i=0}^T 0.5 x' l_{xx,i} x + l_{x,i} x + 0.5 u' l_{uu,i} u + l_{u,i} u + x' l_{xu,i} u + l_x = np.zeros((T, n)) + l_xx = np.zeros((T, n, n)) + l_u = np.zeros((T-1, m)) + l_uu = np.zeros((T-1, m, m)) + l_xu = np.zeros((T-1, n, m)) + + # The cost-to-go is defined by a quadratic function: 0.5 x' Q_{xx,i} x + Q_{x,i} x + ... + Q_xx = np.zeros((T-1, n, n)) + Q_x = np.zeros((T-1, n)) + Q_uu = np.zeros((T-1, m, m)) + Q_u = np.zeros((T-1, m)) + Q_xu = np.zeros((T-1, n, m)) + + x = casadi.SX.sym('x',n,1) + u = casadi.SX.sym('u',m,1) + + running_cost = -self.runningSingleModel.cost(x, u) + terminal_cost = -self.terminalModel.cost(x, u) + + running_cost_xx, running_cost_x = casadi.hessian(running_cost,x) + running_cost_uu, running_cost_u = casadi.hessian(running_cost,u) + running_cost_xu = casadi.jacobian(casadi.jacobian(running_cost,x),u) + terminal_cost_xx, terminal_cost_x = casadi.hessian(terminal_cost,x) + + fun_running_cost_x = casadi.Function('fun_running_cost_x', [x], [running_cost_x], ['x'], ['running_cost_x']) + fun_running_cost_xx = casadi.Function('fun_running_cost_xx', [x], [running_cost_xx], ['x'], ['running_cost_xx']) + fun_running_cost_xu = casadi.Function('fun_running_cost_xu', [x,u],[running_cost_xu], ['x','u'], ['running_cost_xu']) + fun_running_cost_u = casadi.Function('fun_running_cost_u', [u], [running_cost_u], ['u'], ['running_cost_u']) + fun_running_cost_uu = casadi.Function('fun_running_cost_uu', [u], [running_cost_uu], ['u'], ['running_cost_uu']) + fun_terminal_cost_x = casadi.Function('fun_terminal_cost_x', [x], [terminal_cost_x], ['x'], ['terminal_cost_x']) + fun_terminal_cost_xx = casadi.Function('fun_terminal_cost_xx',[x], [terminal_cost_xx], ['x'], ['terminal_cost_xx']) + + # The Value function is defined by a quadratic function: 0.5 x' V_{xx,i} x + V_{x,i} x + V_xx = np.zeros((T, n, n)) + V_x = np.zeros((T, n+1)) + + # Dynamics derivatives w.r.t. x and u + A = np.zeros((T-1, n, n)) + B = np.zeros((T-1, n, m)) + + # Initialize value function + l_x[-1,:], l_xx[-1,:,:] = np.reshape(fun_terminal_cost_x(X_bar[-1,:]),n), fun_terminal_cost_xx(X_bar[-1,:]) + V_xx[T-1,:,:] = l_xx[-1,:,:] + V_x[T-1,:-1] = l_x[-1,:] + + for i in range(T-2, -1, -1): + # Compute dynamics Jacobians + A[i,:,:], B[i,:,:] = self.env.augmented_derivative(X_bar[i,:], U_bar[i,:]) + + # Compute the gradient of the cost function at X=X_bar + l_x[i,:], l_xx[i,:,:] = np.reshape(fun_running_cost_x(X_bar[i,:]),n), fun_running_cost_xx(X_bar[i,:]) + l_u[i,:],l_uu[i,:,:] = np.reshape(fun_running_cost_u(U_bar[i,:]),m), fun_running_cost_uu(U_bar[i,:]) + l_xu[i,:,:] = fun_running_cost_xu(X_bar[i,:], U_bar[i,:]) + + # Compute regularized cost-to-go + Q_x[i,:] = l_x[i,:] + A[i,:,:].T @ V_x[i+1,:-1] + Q_u[i,:] = l_u[i,:] + B[i,:,:].T @ V_x[i+1,:-1] + Q_xx[i,:,:] = l_xx[i,:,:] + A[i,:,:].T @ V_xx[i+1,:,:] @ A[i,:,:] + Q_uu[i,:,:] = l_uu[i,:,:] + B[i,:,:].T @ V_xx[i+1,:,:] @ B[i,:,:] + Q_xu[i,:,:] = l_xu[i,:,:] + A[i,:,:].T @ V_xx[i+1,:,:] @ B[i,:,:] + + Qbar_uu = Q_uu[i,:,:] + mu*np.identity(m) + Qbar_uu_pinv = np.linalg.pinv(Qbar_uu) + + # Compute the derivative of the Value function w.r.t. x + V_x[i,:-1] = Q_x[i,:] - Q_xu[i,:,:] @ Qbar_uu_pinv @ Q_u[i,:] + V_xx[i,:] = Q_xx[i,:] - Q_xu[i,:,:] @ Qbar_uu_pinv @ Q_xu[i,:,:].T + + return V_x \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_0/conf_car.py b/Results Double Integrator/Results set test/Code/N_try_0/conf_car.py new file mode 100644 index 0000000..c4d72bb --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/conf_car.py @@ -0,0 +1,203 @@ +import math +import numpy as np + +system_id = 'car' + +''' CACTO parameters ''' +EP_UPDATE = 250 # Number of episodes before updating critic and actor +NUPDATES = 260000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 38000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 500 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 10000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 1e2 # Distance from target weight +w_u = 1e1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Car/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Car/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Car/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Car/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Car/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Car/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Car/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' # Link mass + +### State parameters +nb_state = 5 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 5 +na = 2 +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, -math.pi, -10, -3, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, math.pi, 10, 3, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, math.pi, 10, 3, int(NSTEPS*dt)]) # Array used to normalize states +# state: x, y, theta, v, a, t + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-10.0, 10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-10.0, -10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([12.0, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([12.0, -2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([15.0, 0.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +bound_actions = 0 +omega_lower_bound = -2 # Action lower bound +omega_upper_bound = 2 +jerk_lower_bound = -1 +jerk_upper_bound = 1 # Action upper bound +u_min = np.array([omega_lower_bound, jerk_lower_bound]) # Action lower bound vector +u_max = np.array([omega_upper_bound, jerk_upper_bound]) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_0/conf_car_park.py b/Results Double Integrator/Results set test/Code/N_try_0/conf_car_park.py new file mode 100644 index 0000000..05c66c1 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/conf_car_park.py @@ -0,0 +1,221 @@ +import math +import numpy as np + +system_id = 'car_park' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 260000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 38000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/2) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 10000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 0 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -10 # X coord center ellipse 1 +YC1 = 6.75 # Y coord center ellipse 1 +A1 = 17 # Width ellipse 1 +B1 = 4.5 # Height ellipse 1 +XC2 = 10 # X coord center ellipse 2 +YC2 = 6.75 # Y coord center ellipse 2 +A2 = 17 # Width ellipse 2 +B2 = 4.5 # Height ellipse 2 +XC3 = 0 # X coord center ellipse 2 +YC3 = -2 # Y coord center ellipse 2 +A3 = 40 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +L = 4.35 +W = 2 +L_delta = 2.63 +tau_delta = 1 + +check_points_BF = np.array([[-L/2, W/2], + [-L/2+L/3, W/2], + [-L/2+2/3*L, W/2], + [L/2, W/2], + [L/2, 0], + [L/2, -W/2], + [-L/2+2/3*L, -W/2], + [-L/2+L/3, -W/2], + [-L/2, -W/2], + [-L/2, 0]]) # Check points in BF # Check points in BF + +### Weigths +w_d = 1e2 # Distance from target weight +w_u = 1e1 # Control effort weight +w_peak = 1e6 # Target threshold weight +w_ob = 5e4 # Obstacle weight +w_v = 1e2 # Velocity weight +delta_bound = 2*np.pi/6 +w_delta_bound = 0 # Delta bound weight (not implemented) +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u, w_delta_bound]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, w_v, w_ob, w_ob, w_ob, 0, w_delta_bound]) # Terminal cost weights vector + +k_db = 50 + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 1 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = 0 # Target x position +y_des = 6.75 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Car Park/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Car Park/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Car Park/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Car Park/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Car Park/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Car Park/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Car Park/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' # Link mass + +### State parameters +nb_state = 5 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 5 +na = 2 + +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-10, 1.5, -math.pi/6, 0, 0, 0]) #1.5 #-2.5 # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 10, 3, math.pi/6, 0, 0, (NSTEPS-1)*dt]) #3.0 #10 # State upper bound initial configuration array +state_norm_arr = np.array([10, 3, math.pi, 10, math.pi/6, int(NSTEPS*dt)]) # Array used to normalize states +# state: x, y, theta, v, delta, t + +# initial configurations for plot.rollout() +init_states_sim = [np.array([-9.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-5.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-2.5 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([0.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([2.5 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([5.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([9.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +acc_lower_bound = -3 # Action lower bound +acc_upper_bound = 3 +delta_dot_lower_bound = -1 +delta_dot_upper_bound = 1 # Action upper bound +u_min = np.array([acc_lower_bound, delta_dot_lower_bound]) # Action lower bound vector +u_max = np.array([acc_upper_bound, delta_dot_upper_bound]) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-11, 11], [-2.5, 10]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_0/conf_double_integrator.py b/Results Double Integrator/Results set test/Code/N_try_0/conf_double_integrator.py new file mode 100644 index 0000000..03b56ca --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/conf_double_integrator.py @@ -0,0 +1,213 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'double_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 50000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 18000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 200 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-4 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 1 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Double Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Double Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Double Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Double Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Double Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Double Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Double Integrator/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Robot upload data +URDF_FILENAME = "double_integrator.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + +q_init, v_init = np.array([-5, 0]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max) + +### State parameters +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, dt]) # State lower bound vector +x_init_min = np.array([-15, -15, -6, -6, dt]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, 6, 6, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, 6, 6, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([12.0, 2.0, 0.0, 0.0, 0.0]), + np.array([12.0, -2.0, 0.0, 0.0, 0.0]), + np.array([15.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = robot.na # Action size +tau_lower_bound = -2 # Action lower bound +tau_upper_bound = 2 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-15, 15], [-15, 15]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_0/conf_manipulator.py b/Results Double Integrator/Results set test/Code/N_try_0/conf_manipulator.py new file mode 100644 index 0000000..6b5d539 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/conf_manipulator.py @@ -0,0 +1,225 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'manipulator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 380000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 50000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network + nsteps_TD_N = int(NSTEPS/2) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 15000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 0 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 24000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' + +NH1 = 256 # 1st hidden layer size +NH2 = 256 # 2nd hidden layer size + +LR_SCHEDULE = 1 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 1e4 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, w_v, w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 50 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -20.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Manipulator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Manipulator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Manipulator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Manipulator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Manipulator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Manipulator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Manipulator/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Robot upload data +URDF_FILENAME = "planar_manipulator_3dof.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + +q_init, v_init = np.array([math.pi, math.pi, math.pi]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max) + +### System configuration parameters +x_base = -7.0 # x coord base +y_base = 0.0 # y coord base + +### State parameters +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-math.pi, -math.pi, -math.pi, -math.pi/4, -math.pi/4, -math.pi/4, 0]) # State lower bound initial configuration vector +x_max = np.array([ np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ math.pi, math.pi, math.pi, math.pi/4, math.pi/4, math.pi/4, (NSTEPS-1)*dt]) # State upper bound initial configuration vector +state_norm_arr = np.array([15,15,15,10,10,10,int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([math.pi/4, -math.pi/8, -math.pi/8, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, math.pi/8, math.pi/8, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + #np.array([ 1.34565955, -2.39833441, 0.87800266, 0., 0., 0., 0. ]), + #np.array([-1.34565955, 2.39833441, -0.87800266, 0., 0., 0., 0. ]), + np.array([-1.55135003, 2.93707696, -1.3025857 , 0., 0., 0., 0. ]), + np.array([ 1.55135003, -2.93707696, 1.3025857 , 0., 0., 0., 0. ]), + np.array([-1.31811607, 2.63623214, -1.31811607, 0., 0., 0., 0. ]), + np.array([-0.98843209, 1.97686418, -0.98843209, 0., 0., 0., 0. ])] + #np.array([-1.05348883, 1.9057266 , -0.61849459, 0., 0., 0., 0. ])] + #np.array([ 1.05348883, -1.9057266 , 0.61849459, 0., 0., 0., 0. ])] + +### Action parameters +nb_action = robot.na # Action size +tau_lower_bound = -200 # Action lower bound +tau_upper_bound = 200 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-41, 31], [-35, 35]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_0/conf_single_integrator.py b/Results Double Integrator/Results set test/Code/N_try_0/conf_single_integrator.py new file mode 100644 index 0000000..02f767d --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/conf_single_integrator.py @@ -0,0 +1,200 @@ +import numpy as np + +system_id = 'single_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 100000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 25000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Single Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Single Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Single Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Single Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Single Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Single Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = 'set prova b' +NNs_path_rec = './Results Single Integrator/Results {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = 0 +update_step_counter_rec = 12000 + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + + + +### State parameters +nb_state = 2 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 2 +na = 2 +x_min = np.array([-np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0]), + np.array([10.0, 10.0, 0.0]), + np.array([-10.0, 10.0, 0.0]), + np.array([-10.0, -10.0, 0.0]), + np.array([12.0, 2.0, 0.0]), + np.array([12.0, -2.0, 0.0]), + np.array([15.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +tau_lower_bound = -6 # Action lower bound +tau_upper_bound = 6 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_0/conf_ur5.py b/Results Double Integrator/Results set test/Code/N_try_0/conf_ur5.py new file mode 100644 index 0000000..1521ac0 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/conf_ur5.py @@ -0,0 +1,227 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'ur5' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 380000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 50000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + # 2nd hidden layer size + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +# Obstacles parameters +XC1 = 0.0 # X coord center ellipse 1 +YC1 = 0.25 # Y coord center ellipse 1 +ZC1 = 0.2 +ell1_center = [XC1, YC1, ZC1] +A1 = 0.5 # Width ellipse 1 +B1 = 0.2 # Height ellipse 1 +C1 = 0.34 +XC2 = 0.2 # X coord center ellipse 2 +YC2 = 0.425 # Y coord center ellipse 2 +ZC2 = 0.2 +ell2_center = [XC2, YC2, ZC2] +A2 = 0.4 # Width ellipse 2 +B2 = 0.14 # Height ellipse 2 +C2 = 0.34 +XC3 = -0.2 # X coord center ellipse 2 +YC3 = 0.425 # Y coord center ellipse 2 +ZC3 = 0.2 +ell3_center = [XC3, YC3, ZC3] +A3 = 0.4 # Width ellipse 2 +B3 = 0.14 # Height ellipse 2 +C3 = 0.34 +obs_param = np.array([XC1, YC1, ZC1, XC2, YC2, ZC2, XC3, YC3, ZC3, A1, B1, C1, A2, B2, C2, A3, B3, C3]) # Obstacle parameters vector + +# Weigths +w_d = 100 # Distance from target weight +w_u = 1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +# SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +# Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +# Target parameters +x_des = 0.0 # Target x position +y_des = 0.425 # Target y position +z_des = 0.2 +TARGET_STATE = np.array([x_des, y_des, z_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results UR5/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results UR5/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results UR5/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results UR5/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results UR5/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results UR5/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results UR5/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + + + +''' System parameters ''' +# Robot upload data +URDF_FILENAME = "ur5_robot.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +# Dynamics parameters' +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' +use_viewer = False +simulate_real_time = True +show_floor = False +PRINT_T = 1 # print every PRINT_N time steps +DISPLAY_T = 0.02 # update robot configuration in viewer every DISPLAY_N time steps +# CAMERA_TRANSFORM = [3.6914889812469482, 0.4583563506603241, -0.05435386672616005, 0.48037904500961304, 0.5339481830596924, 0.5137122273445129, 0.4692920446395874] +CAMERA_TRANSFORM = [0.36461642384529114, 0.8866147994995117, 2.579286575317383, 0.03584412857890129, 0.14833784103393555, 0.9481053948402405, 0.27893954515457153] + +q_init, v_init = np.array([0.,-math.pi/2,0.,0.,0.,0.]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max, use_viewer, DISPLAY_T, CAMERA_TRANSFORM, show_floor) + +# State parameters +dt = 0.01 # Timestep +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-math.pi, -math.pi, -math.pi, -math.pi, -math.pi, -math.pi, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, 0]) # State lower bound initial configuration vector +x_max = np.array([ np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ math.pi, math.pi, math.pi, math.pi, math.pi, math.pi, math.pi/4, math.pi/4, math.pi/4, math.pi/4, math.pi/4, math.pi/4, (NSTEPS-1)*dt]) # State upper bound initial configuration vector +state_norm_arr = np.array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([math.pi/4, -math.pi/8, -math.pi/8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, math.pi/8, math.pi/8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])] + +# Action parameters +nb_action = robot.na # Action size +u_min = np.array([-150, -150, -150, -28, -28, -28]) # Action lower bound vector +u_max = np.array([150, 150, 150, 28, 28, 28]) # Action upper bound vector +w_b = 1/w_u + + +fig_ax_lim = np.array([[-3, 3], [-3, 3]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + +profile = 0 + +env_RL = 0 diff --git a/Results Double Integrator/Results set test/Code/N_try_0/environment.py b/Results Double Integrator/Results set test/Code/N_try_0/environment.py new file mode 100644 index 0000000..87bd97f --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/environment.py @@ -0,0 +1,816 @@ +import math +import mpmath +import random +import numpy as np +import tensorflow as tf +import pinocchio as pin + +from utils import * + +class Env: + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param simu : (RobotSimulator instance) + :param x_init_min : (float array) State lower bound initial configuration array + :param x_init_max : (float array) State upper bound initial configuration array + :param x_min : (float array) State lower bound vector + :param x_max : (float array) State upper bound vector + :param u_min : (float array) Action lower bound array + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + ''' + + self.conf = conf + + self.nq = conf.nq + self.nv = conf.nv + self.nx = conf.nx + self.nu = conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + def reset(self): + ''' Choose initial state uniformly at random ''' + state = np.zeros(self.conf.nb_state) + + time = random.uniform(self.conf.x_init_min[-1], self.conf.x_init_max[-1]) + for i in range(self.conf.nb_state-1): + state[i] = random.uniform(self.conf.x_init_min[i], self.conf.x_init_max[i]) + state[-1] = self.conf.dt*round(time/self.conf.dt) + + return state + + def check_ICS_feasible(self, state): + ''' Check if ICS is not feasible ''' + # check if ee is in the obstacles + p_ee = self.get_end_effector_position(state) + + ellipse1 = ((p_ee[0] - self.conf.XC1)**2) / ((self.conf.A1 / 2)**2) + ((p_ee[1] - self.conf.YC1)**2) / ((self.conf.B1 / 2)**2) + ellipse2 = ((p_ee[0] - self.conf.XC2)**2) / ((self.conf.A2 / 2)**2) + ((p_ee[1] - self.conf.YC2)**2) / ((self.conf.B2 / 2)**2) + ellipse3 = ((p_ee[0] - self.conf.XC3)**2) / ((self.conf.A3 / 2)**2) + ((p_ee[1] - self.conf.YC3)**2) / ((self.conf.B3 / 2)**2) + + feasible_flag = ellipse1 > 1 and ellipse2 > 1 and ellipse3 > 1 + + return feasible_flag + + def step(self, weights, state, action): + ''' Return next state and reward ''' + # compute next state + state_next = self.simulate(state, action) + + # compute reward + reward = self.reward(weights, state, action) + + return (state_next, reward) + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + # Simulate control action + self.conf.simu.simulate(np.copy(state[:-1]), action, self.conf.dt, 1) ### Explicit Euler ### + + # Return next state + state_next[:self.nq], state_next[self.nq:self.nx] = np.copy(self.conf.simu.q), np.copy(self.conf.simu.v) + state_next[-1] = state[-1] + self.conf.dt + + return state_next + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Create robot model in Pinocchio with q_init as initial configuration + q_init = state[:self.nq] + v_init = state[self.nq:self.nx] + + # Dynamics gradient w.r.t control (1st order euler) + pin.computeABADerivatives(self.conf.robot.model, self.conf.robot.data, np.copy(q_init), np.copy(v_init), action) + + Fu = np.zeros((self.nx+1, self.nu)) + Fu[self.nv:-1, :] = self.conf.robot.data.Minv + Fu[:self.nx, :] *= self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + q = state[:self.nq] + v = state[self.nq:self.nx] + + # Compute Jacobians for continuous time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + pin.computeABADerivatives(self.conf.robot.model, self.conf.robot.data, q, v, action) + + Fx[:self.nv, :self.nv] = 0.0 + Fx[:self.nv, self.nv:self.nx] = np.identity(self.nv) + Fx[self.nv:self.nx, :self.nv] = self.conf.robot.data.ddq_dq + Fx[self.nv:self.nx, self.nv:self.nx] = self.conf.robot.data.ddq_dv + Fu[self.nv:self.nx, :] = self.conf.robot.data.Minv + + # Convert them to discrete time + Fx = np.identity(self.conf.nb_state-1) + self.conf.dt * Fx + Fu *= self.conf.dt + + return Fx, Fu + + def simulate_batch(self, state, action): + ''' Simulate dynamics using tensors and compute its gradient w.r.t control. Batch-wise computation ''' + state_next = np.array([self.simulate(s, a) for s, a in zip(state, action)]) + + return tf.convert_to_tensor(state_next, dtype=tf.float32) + + def derivative_batch(self, state, action): + ''' Simulate dynamics using tensors and compute its gradient w.r.t control. Batch-wise computation ''' + Fu = np.array([self.derivative(s, a) for s, a in zip(state, action)]) + + return tf.convert_to_tensor(Fu, dtype=tf.float32) + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + q = state[:self.nq] + + RF = self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id) + + H = self.conf.robot.framePlacement(q, RF, recompute) + + p = H.translation + + return p + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + +class SingleIntegrator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[0,0] = self.conf.dt + Fu[1,1] = self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0], + [0, 1]]) + + Fu[0,0] = self.conf.dt + Fu[1,1] = self.conf.dt + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*action[0] + state_next[1] = state[1] + self.conf.dt*action[1] + state_next[2] = state[2] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + + return p + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class DoubleIntegrator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = [self.get_end_effector_position(state)[i] for i in range(2)] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = np.math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class Car(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[2,0] = self.conf.dt + Fu[4,1] = self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0, -self.conf.dt*state[3]*math.sin(state[2]) -self.conf.dt**2*state[4]*math.sin(state[2])/2, self.conf.dt*math.cos(state[2]), self.conf.dt**2*math.cos(state[2])/2], + [0, 1, self.conf.dt*state[3]*math.cos(state[2]) +self.conf.dt**2*state[4]*math.cos(state[2])/2, self.conf.dt*math.sin(state[2]), self.conf.dt**2*math.sin(state[2])/2], + [0, 0, 1, 0, 0], + [0, 0, 0, 1, self.conf.dt], + [0, 0, 0, 0, 1]]) + + Fu[2,0] = self.conf.dt + Fu[4,1] = self.conf.dt + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*state[3]*tf.cos(state[2]) + self.conf.dt**2*state[4]*tf.cos(state[2])/2 + state_next[1] = state[1] + self.conf.dt*state[3]*tf.sin(state[2]) + self.conf.dt**2*state[4]*tf.sin(state[2])/2 + state_next[2] = state[2] + self.conf.dt*action[0] + state_next[3] = state[3] + self.conf.dt*state[4] + state_next[4] = state[4] + self.conf.dt*action[1] + state_next[5] = state[5] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + + return p + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class CarPark(Car): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def check_ICS_feasible(self, state): + ''' Check if ICS is not feasible ''' + # check if ee is in the obstacles + x_ee, y_ee = self.get_end_effector_position(state)[:2] + theta_ee = state[2] + + for i in range(len(self.conf.check_points_BF)): + check_points_WF_i = np.array([x_ee, y_ee]) + np.array([[math.cos(theta_ee), -math.sin(theta_ee)], [math.sin(theta_ee), math.cos(theta_ee)]]).dot(self.conf.check_points_BF[i,:]) + obs_1 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC1,self.YC1,self.A1,self.B1) + obs_2 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC2,self.YC2,self.A2,self.B2) + obs_3 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC3,self.YC3,self.A3,self.B3) + + feasible_flag = obs_1 < 0.5 and obs_2 < 0.5 and obs_3 < 0.5 + if feasible_flag == 0: + return feasible_flag + + return feasible_flag + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[3,0] = self.conf.dt + Fu[4,1] = self.conf.dt/self.conf.tau_delta + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0, -self.conf.dt*state[3]*math.sin(state[2]), self.conf.dt*math.cos(state[2]), 0], + [0, 1, self.conf.dt*state[3]*math.cos(state[2]), self.conf.dt*math.sin(state[2]), 0], + [0, 0, 1, self.conf.dt*math.tan(state[4])/self.conf.L_delta, self.conf.dt*state[3]*mpmath.sec(state[4])**2/self.conf.L_delta], + [0, 0, 0, 1, 0], + [0, 0, 0, 0, 1]]) + + Fu[3,0] = self.conf.dt + Fu[4,1] = self.conf.dt/self.conf.tau_delta + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*state[3]*math.cos(state[2]) + state_next[1] = state[1] + self.conf.dt*state[3]*math.sin(state[2]) + state_next[2] = state[2] + self.conf.dt*state[3]*math.tan(state[4])/self.conf.L_delta + state_next[3] = state[3] + self.conf.dt*action[0] + state_next[4] = state[4] + self.conf.dt*action[1]/self.conf.tau_delta + state_next[5] = state[5] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + np.array([[math.cos(state[2]), -math.sin(state[2])], [math.sin(state[2]), math.cos(state[2])]]).dot(np.array([self.conf.L_delta/2,0])) + + return p + + def obs_cost_fun(self,x,y,x_step,y_step,Wx,Wy,fv=1,k=50): + k = self.conf.k_db + + term1 = 4 + 4 * (y - y_step + Wy/2)**2 * k**2 + term2 = 4 + 4 * (y - y_step - Wy/2)**2 * k**2 + term3 = 4 + 4 * (x - x_step + Wx/2)**2 * k**2 + term4 = 4 + 4 * (x - x_step - Wx/2)**2 * k**2 + obs_cost = (term1)**(-1/2) * fv * (-np.sqrt(term2) / 2 + (y - y_step - Wy/2) * k) * (term3)**(-1/2) * (term2)**(-1/2) * (np.sqrt(term1) / 2 + (y - y_step + Wy/2) * k) * (term4)**(-1/2) * (np.sqrt(term3) / 2 + (x - x_step + Wx/2) * k) * (-np.sqrt(term4) / 2 + (x - x_step - Wx/2) * k) + + return obs_cost + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + theta_ee = state[2] + + obs_cost = 0 + check_points_WF = np.dot(np.array([[np.cos(theta_ee), -np.sin(theta_ee)], [np.sin(theta_ee), np.cos(theta_ee)]]), self.conf.check_points_BF.T).T + np.array([x_ee, y_ee]) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC1, self.YC1, self.A1, self.B1)) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC2, self.YC2, self.A2, self.B2)) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC3, self.YC3, self.A3, self.B3)) + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 + 0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 + 0.1) - math.sqrt(0.1) - 0.1 )) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*state[3]**2 - weights[3]*obs_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class Manipulator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = [self.get_end_effector_position(state)[i] for i in range(2)] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term penalizing the FINAL joint velocity + if weights[2] != 0: + vel_cost = state[self.nq:self.nx].dot(state[self.nq:self.nx]) + else: + vel_cost = 0 + + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*vel_cost - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) #- weights[2]*vel_cost + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class UR5(Env): + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.ZC1 = self.conf.obs_param[2] + self.XC2 = self.conf.obs_param[3] + self.YC2 = self.conf.obs_param[4] + self.ZC2 = self.conf.obs_param[5] + self.XC3 = self.conf.obs_param[6] + self.YC3 = self.conf.obs_param[7] + self.ZC3 = self.conf.obs_param[8] + + self.A1 = self.conf.obs_param[9] + self.B1 = self.conf.obs_param[10] + self.C1 = self.conf.obs_param[11] + self.A2 = self.conf.obs_param[12] + self.B2 = self.conf.obs_param[13] + self.C2 = self.conf.obs_param[14] + self.A3 = self.conf.obs_param[15] + self.B3 = self.conf.obs_param[16] + self.C3 = self.conf.obs_param[17] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee, z_ee = self.get_end_effector_position(state) + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) + ((z_ee-self.ZC1)**2)/((self.C1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) + ((z_ee-self.ZC2)**2)/((self.C2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) + ((z_ee-self.ZC3)**2)/((self.C3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((z_ee-self.TARGET_STATE[2])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + if action is not None: + u_cost = action.dot(action) + else: + u_cost = 0 + + # Term penalizing the FINAL joint velocity + vel_cost = state[self.nq:self.nx].dot(state[self.nq:self.nx]) + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + (z_ee-self.TARGET_STATE[2])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*vel_cost - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) diff --git a/Results Double Integrator/Results set test/Code/N_try_0/environment_TO.py b/Results Double Integrator/Results set test/Code/N_try_0/environment_TO.py new file mode 100644 index 0000000..0cf4b63 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/environment_TO.py @@ -0,0 +1,778 @@ +import sys +import math +import casadi +import numpy as np +import pinocchio.casadi as cpin + +class SingleIntegrator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + p_ee[-1] = 0 + + return p_ee + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*u[0] + x_next[1] = x[1] + self.conf.dt*u[1] + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class DoubleIntegrator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class Car_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + p_ee[-1] = 0 + + return p_ee + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*x[3]*casadi.cos(x[2]) + self.conf.dt**2*x[4]*casadi.cos(x[2])/2 + x_next[1] = x[1] + self.conf.dt*x[3]*casadi.sin(x[2]) + self.conf.dt**2*x[4]*casadi.sin(x[2])/2 + x_next[2] = x[2] + self.conf.dt*u[0] + x_next[3] = x[3] + self.conf.dt*x[4] + x_next[4] = x[4] + self.conf.dt*u[1] + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class CarPark_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + np.array([[np.cos(cx[2]), -np.sin(cx[2])], [np.sin(cx[2]), np.cos(cx[2])]]).dot(np.array([self.conf.L_delta/2,0])) + p_ee[-1] = 0 + + return p_ee + + def obs_cost_fun(self, x, y, x_clip, y_clip, Wx, Wy, fv=1, k=50): + k = self.conf.k_db + obs_cost = (4 + 4 * (y - y_clip + Wy / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * fv * (-np.sqrt(4 + 4 * (y - y_clip - Wy / 2) ** 2 * k ** 2) / 2 + (y - y_clip - Wy / 2) * k) * (4 + 4 * (x - x_clip + Wx / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (4 + 4 * (y - y_clip - Wy / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (np.sqrt(4 + 4 * (y - y_clip + Wy / 2) ** 2 * k ** 2) / 2 + (y - y_clip + Wy / 2) * k) * (4 + 4 * (x - x_clip - Wx / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (np.sqrt(4 + 4 * (x - x_clip + Wx / 2) ** 2 * k ** 2) / 2 + (x - x_clip + Wx / 2) * k) * (-np.sqrt(4 + 4 * (x - x_clip - Wx / 2) ** 2 * k ** 2) / 2 + (x - x_clip - Wx / 2) * k) + + return obs_cost + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def rotation_matrix(self, angle): + ''' Compute the 2x2 rotation matrix for a given angle ''' + cos_theta = casadi.cos(angle) + sin_theta = casadi.sin(angle) + + rotation_mat = casadi.vertcat(casadi.horzcat(cos_theta, -sin_theta), + casadi.horzcat(sin_theta, cos_theta)) + return rotation_mat + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee_tmp = self.p_ee(x) + p_ee = p_ee_tmp[:2] + theta_ee = x[2] + + ### Penalties representing the obstacle ### + obs_cost = 0 + check_points_WF = casadi.mtimes(self.rotation_matrix(theta_ee), self.conf.check_points_BF.T).T + casadi.repmat(p_ee.T, self.conf.check_points_BF.shape[0], 1) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC1, self.YC1, self.A1, self.B1)) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC2, self.YC2, self.A2, self.B2)) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC3, self.YC3, self.A3, self.B3)) + + ### Control effort term ### + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 2*0.1 - 2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*x[3]**2 + self.weights[3]*obs_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*x[3]*casadi.cos(x[2]) + x_next[1] = x[1] + self.conf.dt*x[3]*casadi.sin(x[2]) + x_next[2] = x[2] + self.conf.dt*x[3]*casadi.tan(x[4])/self.conf.L_delta + x_next[3] = x[3] + self.conf.dt*u[0] + x_next[4] = x[4] + self.conf.dt*u[1]/self.conf.tau_delta + + return x_next + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class Manipulator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class UR5_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.ZC1 = self.conf.obs_param[2] + self.XC2 = self.conf.obs_param[3] + self.YC2 = self.conf.obs_param[4] + self.ZC2 = self.conf.obs_param[5] + self.XC3 = self.conf.obs_param[6] + self.YC3 = self.conf.obs_param[7] + self.ZC3 = self.conf.obs_param[8] + + self.A1 = self.conf.obs_param[9] + self.B1 = self.conf.obs_param[10] + self.C1 = self.conf.obs_param[11] + self.A2 = self.conf.obs_param[12] + self.B2 = self.conf.obs_param[13] + self.C2 = self.conf.obs_param[14] + self.A3 = self.conf.obs_param[15] + self.B3 = self.conf.obs_param[16] + self.C3 = self.conf.obs_param[17] + + self.TARGET_STATE = self.conf.TARGET_STATE + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC1)**2)/((self.conf.A1/2)**2) + ((p_ee[1]-self.conf.YC1)**2)/((self.conf.B1/2)**2) + ((p_ee[2]-self.conf.ZC1)**2)/((self.conf.C1/2)**2) - 1.0)) + 1)/self.conf.alpha + ell2_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC2)**2)/((self.conf.A2/2)**2) + ((p_ee[1]-self.conf.YC2)**2)/((self.conf.B2/2)**2) + ((p_ee[2]-self.conf.ZC2)**2)/((self.conf.C2/2)**2) - 1.0)) + 1)/self.conf.alpha + ell3_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC3)**2)/((self.conf.A3/2)**2) + ((p_ee[1]-self.conf.YC3)**2)/((self.conf.B3/2)**2) + ((p_ee[2]-self.conf.ZC3)**2)/((self.conf.C3/2)**2) - 1.0)) + 1)/self.conf.alpha + + ### Control effort term ### + u_cost = 0 + for i in range(self.nu): + u_cost += u[i]**2 + self.conf.w_b*(u[i]/self.conf.u_max[i])**10 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.conf.alpha2*-(np.sqrt((p_ee[0]-self.conf.TARGET_STATE[0])**2 +0.1) - np.sqrt(0.1) - 0.1 + np.sqrt((p_ee[1]-self.conf.TARGET_STATE[1])**2 +0.1) - np.sqrt(0.1) - 0.1 + np.sqrt((p_ee[2]-self.conf.TARGET_STATE[2])**2 +0.1) - np.sqrt(0.1) - 0.1)) + 1)/self.conf.alpha2 + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.conf.TARGET_STATE[0])**2 + (p_ee[1]-self.conf.TARGET_STATE[1])**2 + (p_ee[2]-self.conf.TARGET_STATE[2])**2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_0/generate_tests_set_script.py b/Results Double Integrator/Results set test/Code/N_try_0/generate_tests_set_script.py new file mode 100644 index 0000000..4c5f781 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/generate_tests_set_script.py @@ -0,0 +1,26 @@ +### Create file to be executed ### +import os +import shutil + +system_id = 'car' +system_id_short = 'C' + +n_runs = 10 + +w_S = 1e-3 +info = '{}'.format(1) + +offset = 0 if w_S ==0 else 100 + +seed_list = [29556, 5280, 739, 92, 10, 7298, 14, 264, 22135, 342] + +shutil.copy('Template.sh', '{}{}.sh'.format(system_id_short,info)) +with open('{}{}.sh'.format(system_id_short,info), 'a') as f: + for i in range(1,1 + n_runs): + f.write("nohup python3 -u main.py --system-id='{}' --test-n={} --seed={} --w-S={} > out/{}{}.txt &\n".format(system_id,offset+i,seed_list[i-1], w_S, system_id_short,offset+i)) + f.write("\necho 'Running scripts in parallel'\n") + f.write("echo 'PID of this script is:' $$\n") + f.write("wait\n") + f.write("echo 'Script done running'\n") + +os.makedirs('out', exist_ok=True) \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_0/main.py b/Results Double Integrator/Results set test/Code/N_try_0/main.py new file mode 100644 index 0000000..8121e8c --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/main.py @@ -0,0 +1,254 @@ +import os +import sys +import time +import shutil +import random +import argparse +import importlib +import numpy as np +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # {'0' -> show all logs, '1' -> filter out info, '2' -> filter out warnings} +import tensorflow as tf +from multiprocessing import Pool +from RL import RL_AC +from TO import TO_Casadi +from plot_utils import PLOT +from NeuralNetwork import NN +from replay_buffer import PrioritizedReplayBuffer, ReplayBuffer + +def parse_args(): + ''' Parse the arguments for CACTO training ''' + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + + parser.add_argument('--test-n', type=int, default=0, + help="Test number") + + parser.add_argument('--seed', type=int, default=0, + help="random and tf.random seed") + + parser.add_argument('--system-id', type=str, default='single_integrator', + choices=["single_integrator", "double_integrator", "car", "car_park", "manipulator", "ur5"], + help="System-id (single_integrator, double_integrator, car, manipulator, ur5") + + parser.add_argument('--recover-training-flag', type=bool, default=False, + choices=["True", "False"], + help="Flag to recover training") + ### Not tested ### + #parser.add_argument('--GPU-flag', type=bool, default=False, + # choices=["True", "False"], + # help="Flag to use GPU") + + parser.add_argument('--nb-cpus', type=int, default=10, + help="Number of TO problems solved in parallel") + + parser.add_argument('--w-S', type=float, default=0, + help="Sobolev training - weight of the value related error") + + args = parser.parse_args() + dict_args = vars(args) + + return dict_args + + + + +if __name__ == '__main__': + + args = parse_args() + + ############### Input ############### + N_try = args['test_n'] + + if args['seed'] == None: + seed = random.randint(1,100000) + else: + seed = args['seed'] + tf.random.set_seed(seed) # Set tensorflow seed + random.seed(seed) # Set random seed + + system_id = args['system_id'] + + recover_training_flag = args['recover_training_flag'] + + ### Not tested ### + #GPU_flag = args['GPU_flag'] + #if GPU_flag: + # os.environ["CUDA_VISIBLE_DEVICES"]="-1" + #print(tf.config.experimental.list_physical_devices('GPU')) + + nb_cpus = args['nb_cpus'] + + w_S = args['w_S'] + ######################################################### + + + + # Import configuration file and environment file + system_map = { + 'single_integrator': ('conf_single_integrator', 'SingleIntegrator', 'SingleIntegrator_CAMS'), + 'double_integrator': ('conf_double_integrator', 'DoubleIntegrator', 'DoubleIntegrator_CAMS'), + 'car': ('conf_car', 'Car', 'Car_CAMS'), + 'car_park': ('conf_car_park', 'CarPark', 'CarPark_CAMS'), + 'manipulator': ('conf_manipulator', 'Manipulator', 'Manipulator_CAMS'), + 'ur5': ('conf_ur5', 'UR5', 'UR5_CAMS') + } + try: + conf_module, env_class, env_TO_class = system_map[system_id] + conf = importlib.import_module(conf_module) + Environment = getattr(importlib.import_module('environment'), env_class) + Environment_TO = getattr(importlib.import_module('environment_TO'), env_TO_class) + except KeyError: + print('System {} not found'.format(system_id)) + sys.exit() + + # Create folders to store the results and the trained NNs + for path in conf.path_list: + os.makedirs(path + '/N_try_{}'.format(N_try), exist_ok=True) + os.makedirs(conf.Config_path, exist_ok=True) + + # Save configuration + params = [p for p in conf.__dict__ if not p.startswith("__")] + with open(conf.Config_path + '/config{}.txt'.format(N_try), 'w') as f: + for p in params: + f.write('{} = {}\n'.format(p, conf.__dict__[p])) + f.write('Seed = {}\n'.format(seed)) + f.write('w_S = {}'.format(w_S)) + + shutil.copy('{}.py'.format(conf_module), conf.Config_path + '/' + conf_module + '_{}.py'.format(N_try)) + with open(conf.Config_path + '/' + conf_module + '_{}.py'.format(N_try), 'a') as f: + f.write('\n\n# {}'.format(args)) + + # Copy all file with .py extension from /mydir to /mydestdir + for file in os.listdir("./"): + if file.endswith(".py"): + shutil.copy(os.path.join("./", file), os.path.join(conf.Code_path + '/N_try_{}'.format(N_try), file)) + + # Create empty txt file in Log_path to store the test info + open(conf.Log_path + '/info.txt', 'a').close() + + + + ### Create instances of the used classes ### + env = Environment(conf) # Create environment instances + env_TO = Environment_TO + NN_inst = NN(env, conf, w_S) # Create NN instance + TrOp = TO_Casadi(env, conf, env_TO, w_S) # Create TO instance + RLAC = RL_AC(env, NN_inst, conf, N_try) # Create RL instance + buffer = ReplayBuffer(conf) if conf.prioritized_replay_alpha == 0 else PrioritizedReplayBuffer(conf) # Create an empty (prioritized) replay buffer + plot_fun = PLOT(N_try, env, NN_inst, conf) # Create PLOT instance + + # Set initial weights of the NNs, initialize the counter of the updates and setup NN models + if recover_training_flag: + recover_training = np.array([conf.NNs_path_rec, conf.N_try_rec, conf.update_step_counter_rec]) + update_step_counter = conf.update_step_counter_rec + + RLAC.setup_model(recover_training) + else: + update_step_counter = 0 + + RLAC.setup_model() + + # Save initial weights of the NNs + RLAC.RL_save_weights(update_step_counter) + + # Plot initial rollouts + plot_fun.plot_traj_from_ICS(np.array(conf.init_states_sim), TrOp, RLAC, update_step_counter=update_step_counter,steps=conf.NSTEPS, init=0) + + # Initialize arrays to store the reward history of each episode and the average reward history of last 100 episodes + ep_arr_idx = 0 + ep_reward_arr = np.zeros(conf.NEPISODES-ep_arr_idx)*np.nan + + def compute_sample(args): + ''' Create samples solving TO problems starting from given ICS ''' + ep = args[0] + ICS = args[1] + + # Create initial TO # + init_rand_state, init_TO_states, init_TO_controls, NSTEPS_SH, success_init_flag = RLAC.create_TO_init(ep, ICS) + if success_init_flag == 0: + return None + + # Solve TO problem # + TO_controls, TO_states, success_flag, TO_ee_pos_arr, TO_step_cost, dVdx = TrOp.TO_Solve(init_rand_state, init_TO_states, init_TO_controls, NSTEPS_SH) + if success_flag == 0: + return None + + # Collect experiences + state_arr, partial_reward_to_go_arr, total_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, RL_ee_pos_arr = RLAC.RL_Solve(TO_controls, TO_states, TO_step_cost) + + if conf.env_RL == 0: + RL_ee_pos_arr = TO_ee_pos_arr + + return NSTEPS_SH, TO_controls, TO_ee_pos_arr, dVdx, state_arr.tolist(), partial_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, RL_ee_pos_arr + + def create_unif_TO_init(n_UICS=1): + ''' Create n uniformely distributed ICS ''' + # Create ICS TO # + init_rand_state = env.reset() + + return init_rand_state + + + + ### START TRAINING ### + if conf.profile: + import cProfile, pstats + + profiler = cProfile.Profile() + profiler.enable() + + time_start = time.time() + + for ep in range(conf.NLOOPS): + # Generate and store conf.EP_UPDATE random-uniform ICS + with Pool(nb_cpus) as p: + init_rand_state = p.map(create_unif_TO_init, range(conf.EP_UPDATE)) + + # Generate samples + with Pool(nb_cpus) as p: + tmp = p.map(compute_sample, zip(ep*np.ones(conf.EP_UPDATE), init_rand_state)) + + # Remove unsuccessful TO problems and update EP_UPDATE + tmp = [x for x in tmp if x is not None] + NSTEPS_SH, TO_controls, ee_pos_arr_TO, dVdx, state_arr, partial_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, ee_pos_arr_RL = zip(*tmp) + + # Update the buffer + buffer.add(state_arr, partial_reward_to_go_arr, state_next_rollout_arr, dVdx, done_arr, term_arr) + + # Update NNs + update_step_counter = RLAC.learn_and_update(update_step_counter, buffer, ep) + + # plot Critic value function + #plot_fun.plot_Critic_Value_function(RLAC.critic_model, update_step_counter, system_id) ### + + # Plot rollouts and state and control trajectories + if update_step_counter%conf.plot_rollout_interval_diff_loc == 0 or system_id == 'single_integrator' or system_id == 'double_integrator' or system_id == 'car_park' or system_id == 'car' or system_id == 'manipulator': + print("System: {} - N_try = {}".format(conf.system_id, N_try)) + plot_fun.plot_Critic_Value_function(RLAC.critic_model, update_step_counter, system_id) + plot_fun.plot_traj_from_ICS(np.array(conf.init_states_sim), TrOp, RLAC, update_step_counter=update_step_counter, ep=ep,steps=conf.NSTEPS, init=1) + + # Update arrays to store the reward history and its average + ep_reward_arr[ep_arr_idx:ep_arr_idx+len(tmp)] = ep_return + ep_arr_idx += len(tmp) + + for i in range(len(tmp)): + print("Episode {} ---> Return = {}".format(ep*len(tmp) + i, ep_return[i])) + + if update_step_counter > conf.NUPDATES: + break + + time_end = time.time() + print('Elapsed time: ', time_end-time_start) + + if conf.profile: + profiler.disable() + stats = pstats.Stats(profiler).sort_stats('cumtime') + stats.print_stats() + + # Plot returns + plot_fun.plot_Return(ep_reward_arr) + + # Save networks at the end of the training + RLAC.RL_save_weights() + + # Simulate the final policy + plot_fun.rollout(update_step_counter, RLAC.actor_model, conf.init_states_sim) diff --git a/Results Double Integrator/Results set test/Code/N_try_0/plot_utils.py b/Results Double Integrator/Results set test/Code/N_try_0/plot_utils.py new file mode 100644 index 0000000..55daab0 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/plot_utils.py @@ -0,0 +1,714 @@ +import math +import numpy as np +import tensorflow as tf +import matplotlib.pyplot as plt +from matplotlib import cm, colors +from matplotlib.patches import Ellipse, FancyBboxPatch, Rectangle +from matplotlib.transforms import Affine2D +import mpl_toolkits.mplot3d.art3d as art3d + +class PLOT(): + def __init__(self, N_try, env, NN, conf): + ''' + :input N_try : (Test number) + + :input env : (Environment instance) + + :input conf : (Configuration file) + :param fig_ax_lim : (float array) Figure axis limit [x_min, x_max, y_min, y_max] + :param Fig_path : (str) Figure path + :param NSTEPS : (int) Max episode length + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param NORMALIZE_INPUTS : (bool) Flag to normalize inputs (state) + :param state_norm_array : (float array) Array used to normalize states + :param dt : (float) Timestep + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + ''' + self.env = env + self.NN = NN + self.conf = conf + + self.N_try = N_try + + self.xlim = conf.fig_ax_lim[0].tolist() + self.ylim = conf.fig_ax_lim[1].tolist() + + # Set the ticklabel font size globally + plt.rcParams['xtick.labelsize'] = 22 + plt.rcParams['ytick.labelsize'] = 22 + plt.rcParams.update({'font.size': 20}) + + return + + def plot_obstaces(self, a=1): + if self.conf.system_id == 'car_park': + obs1 = Rectangle((self.conf.XC1-self.conf.A1/2, self.conf.YC1-self.conf.B1/2), self.conf.A1, self.conf.B1, 0.0,alpha=a) + obs1.set_facecolor([30/255, 130/255, 76/255, 1]) + obs2 = Rectangle((self.conf.XC2-self.conf.A2/2, self.conf.YC2-self.conf.B2/2), self.conf.A2, self.conf.B2, 0.0,alpha=a) + obs2.set_facecolor([30/255, 130/255, 76/255, 1]) + obs3 = Rectangle((self.conf.XC3-self.conf.A3/2, self.conf.YC3-self.conf.B3/2), self.conf.A3, self.conf.B3, 0.0,alpha=a) + obs3.set_facecolor([30/255, 130/255, 76/255, 1]) + + #rec1 = FancyBboxPatch((self.conf.XC1-self.conf.A1/2, self.conf.YC1-self.conf.B1/2), self.conf.A1, self.conf.B1,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec1.set_facecolor([30/255, 130/255, 76/255, 1]) + #rec2 = FancyBboxPatch((self.conf.XC2-self.conf.A2/2, self.conf.YC2-self.conf.B2/2), self.conf.A2, self.conf.B2,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec2.set_facecolor([30/255, 130/255, 76/255, 1]) + #rec3 = FancyBboxPatch((self.conf.XC3-self.conf.A3/2, self.conf.YC3-self.conf.B3/2), self.conf.A3, self.conf.B3,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec3.set_facecolor([30/255, 130/255, 76/255, 1]) + else: + obs1 = Ellipse((self.conf.XC1, self.conf.YC1), self.conf.A1, self.conf.B1, 0.0,alpha=a) + obs1.set_facecolor([30/255, 130/255, 76/255, 1]) + obs2 = Ellipse((self.conf.XC2, self.conf.YC2), self.conf.A2, self.conf.B2, 0.0,alpha=a) + obs2.set_facecolor([30/255, 130/255, 76/255, 1]) + obs3 = Ellipse((self.conf.XC3, self.conf.YC3), self.conf.A3, self.conf.B3, 0.0,alpha=a) + obs3.set_facecolor([30/255, 130/255, 76/255, 1]) + + return [obs1, obs2, obs3] + + def plot_Reward(self, plot_obs=0): + x = np.arange(-15, 15, 0.1) + y = np.arange(-10, 10, 0.1) + theta = np.pi/2 + ICS = np.array([np.array([i,j,0]) for i in x for j in y]) + state = np.array([self.compute_ICS(np.array([i,j,0]), 'car')[0] for i in x for j in y]) # for k in theta] + state[:,2] = theta + r = [self.env.reward(self.conf.cost_weights_running, s) for s in state] + mi = min(r) + ma = max(r) + norm = colors.Normalize(vmin=mi,vmax=ma) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + pti = ax.scatter(ICS[:,0], ICS[:,1], norm=norm, c=r, cmap=cm.get_cmap('hot_r')) + plt.colorbar(pti) + + if plot_obs: + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + + # Center and check points of 'car_park' system + #check_points_WF_i = np.array([[math.cos(theta), -math.sin(theta)], [math.sin(theta), math.cos(theta)]]).dot(self.conf.check_points_BF[0,:]) + ICS[0,:2] + #ax.scatter(check_points_WF_i[0], check_points_WF_i[1], c='b') + #for i in range(1,len(self.conf.check_points_BF)): + # check_points_WF_i = np.array([[math.cos(theta), -math.sin(theta)], [math.sin(theta), math.cos(theta)]]).dot(self.conf.check_points_BF[i,:]) + ICS[0,:2] + # ax.scatter(check_points_WF_i[0], check_points_WF_i[1], c='r') + + ax.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5, legend='Goal position') + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.set_title('Plane') + #ax.legend() + ax.grid(True) + plt.show() + + def compute_ICS(self, p_ee, sys_id, theta=None, continue_flag=0): + if sys_id == 'manipulator': + radius = math.sqrt((p_ee[0]-self.conf.x_base)**2+(p_ee[1])**2) + if radius > 30: + continue_flag = 1 + return None, continue_flag + + phi = math.atan2(p_ee[1]-self.conf.y_base,(p_ee[0]-self.conf.x_base)) # SUM OF THE ANGLES FIXED + X3rd_joint = (p_ee[0]-self.conf.x_base) - self.conf.l* math.cos(phi) + Y3rd_joint = (p_ee[1]-self.conf.y_base) - self.conf.l* math.sin(phi) + + if abs(X3rd_joint) <= 1e-6 and abs(Y3rd_joint) <= 1e-6: + continue_flag = 1 + return None, continue_flag + + c2 = (X3rd_joint**2 + Y3rd_joint**2 -2*self.conf.l**2)/(2*self.conf.l**2) + + if p_ee[1] >= 0: + s2 = math.sqrt(1-c2**2) + else: + s2 = -math.sqrt(1-c2**2) + + s1 = ((self.conf.l + self.conf.l*c2)*Y3rd_joint - self.conf.l*s2*X3rd_joint)/(X3rd_joint**2 + Y3rd_joint**2) + c1 = ((self.conf.l + self.conf.l*c2)*X3rd_joint - self.conf.l*s2*Y3rd_joint)/(X3rd_joint**2 + Y3rd_joint**2) + ICS_q0 = math.atan2(s1,c1) + ICS_q1 = math.atan2(s2,c2) + ICS_q2 = phi-ICS_q0-ICS_q1 + + ICS = np.array([ICS_q0, ICS_q1, ICS_q2, 0.0, 0.0, 0.0, 0.0]) + + elif sys_id == 'car': + if theta == None: + theta = 0*np.random.uniform(-math.pi,math.pi) + ICS = np.array([p_ee[0], p_ee[1], theta, 0.0, 0.0, 0.0]) + + elif sys_id == 'car_park': + if theta == None: + #theta = 0*np.random.uniform(-math.pi,math.pi) + theta = np.pi/2 + ICS = np.array([p_ee[0], p_ee[1], theta, 0.0, 0.0, 0.0]) + + elif sys_id == 'double_integrator': + ICS = np.array([p_ee[0], p_ee[1], 0.0, 0.0, 0.0]) + + elif sys_id == 'single_integrator': + ICS = np.array([p_ee[0], p_ee[1], 0.0]) + + return ICS, continue_flag + + def plot_policy(self, tau, x, y, steps, n_updates, diff_loc=0): + ''' Plot policy rollout from a single initial state as well as state and control trajectories ''' + timesteps = self.self.conf.dt*np.arange(steps) + + fig = plt.figure(figsize=(12,8)) + plt.suptitle('POLICY: Discrete model, N try = {} N updates = {}'.format(self.N_try,n_updates), y=1) + + ax1 = fig.add_subplot(2, 2, 1) + ax1.plot(timesteps, x, 'ro', linewidth=1, markersize=1, legedn='x') + ax1.plot(timesteps, y, 'bo', linewidth=1, markersize=1, legend='y') + ax1.set_xlabel('Time [s]') + ax1.set_ylabel('[m]') + ax1.set_title('End-Effector Position') + ax1.legend() + ax1.grid(True) + + col = ['ro', 'bo', 'go'] + ax2 = fig.add_subplot(2, 2, self.conf.nb_action) + for i in range(self.conf.nb_action): + ax2.plot(timesteps, tau[:,i], col[i], linewidth=1, markersize=1,legend='tau{}'.format(i)) + ax2.set_xlabel('Time [s]') + ax2.set_title('Controls') + ax2.legend() + ax2.grid(True) + + ax3 = fig.add_subplot(1, 2, 2) + ax3.plot(x, y, 'ro', linewidth=1, markersize=1) + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax3.add_artist(obs_plot_list[i]) + ax3.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=10) + ax3.set_xlim(self.xlim) + ax3.set_ylim(self.ylim) + ax3.set_aspect('equal', 'box') + ax3.set_xlabel('X [m]') + ax3.set_ylabel('Y [m]') + ax3.set_title('Plane') + ax3.grid(True) + + fig.tight_layout() + + if diff_loc==0: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationSingleInit_{}_{}'.format(self.N_try,n_updates)) + else: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationMultiInit_{}_{}'.format(self.N_try,n_updates)) + + plt.clf() + plt.close(fig) + + def plot_policy_eval(self, p_list, n_updates, diff_loc=0, theta=0): + ''' Plot only policy rollouts from multiple initial states ''' + fig = plt.figure(figsize=(12,8)) + plt.suptitle('POLICY: Discrete model, N try = {} N updates = {}'.format(self.N_try,n_updates), y=1) + + ax = fig.add_subplot(1, 1, 1) + for idx in range(len(p_list)): + ax.plot(p_list[idx][:,0], p_list[idx][:,1], marker='o', linewidth=0.3, markersize=1) + ax.plot(p_list[idx][0,0],p_list[idx][0,1],'ko',markersize=5) + if self.conf.system_id == 'car_park': + theta = p_list[idx][-1,2] + fancybox = FancyBboxPatch((0 - self.conf.L/2, 0 - self.conf.W/2), self.conf.L, self.conf.W, edgecolor='none', alpha=0.5, boxstyle='round,pad=0') + fancybox.set_transform(Affine2D().rotate_deg(np.rad2deg(theta)).translate(p_list[idx][-1,0], p_list[idx][-1,1]) + ax.transData) + ax.add_patch(fancybox) + + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + + ax.plot(self.conf.TARGET_STATE[0],self.conf.TARGET_STATE[1],'b*',markersize=10) + + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.grid(True) + fig.tight_layout() + if diff_loc==0: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationSingleInit_{}_{}'.format(self.N_try,n_updates)) + else: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationMultiInit_{}_{}'.format(self.N_try,n_updates)) + + plt.clf() + plt.close(fig) + + def rollout(self,update_step_cntr, actor_model, init_states_sim, diff_loc=0): + ''' Plot rollout of the actor from some initial states. It generates the results and then calls plot_policy() and plot_policy_eval() ''' + #tau_all_sim = [] + p_ee_all_sim = [] + + returns = {} + + for k in range(len(init_states_sim)): + rollout_controls = np.zeros((self.conf.NSTEPS,self.conf.nb_action)) + rollout_states = np.zeros((self.conf.NSTEPS+1,self.conf.nb_state)) + rollout_p_ee = np.zeros((self.conf.NSTEPS+1,3)) + rollout_episodic_reward = 0 + + rollout_p_ee[0,:] = self.env.get_end_effector_position(init_states_sim[k]) + rollout_states[0,:] = np.copy(init_states_sim[k]) + + for i in range(self.conf.NSTEPS): + rollout_controls[i,:] = tf.squeeze(self.NN.eval(actor_model, np.array([rollout_states[i,:]]))).numpy() + rollout_states[i+1,:], rwrd_sim = self.env.step(self.conf.cost_weights_running, rollout_states[i,:],rollout_controls[i,:]) + rollout_p_ee[i+1,:] = self.env.get_end_effector_position(rollout_states[i+1,:]) + + rollout_p_ee[i+1,-1] = rollout_states[i+1,2] ### !!! ### + + rollout_episodic_reward += rwrd_sim + + if k==0: + print("N try = {}: Simulation Return @ N updates = {} ==> {}".format(self.N_try,update_step_cntr,rollout_episodic_reward)) + + p_ee_all_sim.append(rollout_p_ee) + + returns[init_states_sim[k][0],init_states_sim[k][1]] = rollout_episodic_reward + + self.plot_policy_eval(p_ee_all_sim,update_step_cntr, diff_loc=diff_loc) + + return returns + + def plot_results(self, tau, ee_pos_TO, ee_pos_RL, steps, to=0): + ''' Plot results from TO and episode to check consistency ''' + timesteps = self.conf.dt*np.arange(steps+1) + fig = plt.figure(figsize=(12,8)) + if to: + plt.suptitle('TO EXPLORATION: N try = {}'.format(self.N_try), y=1, fontsize=20) + else: + plt.suptitle('POLICY EXPLORATION: N try = {}'.format(self.N_try), y=1, fontsize=20) + + ax1 = fig.add_subplot(2, 2, 1) + ax1.plot(timesteps, ee_pos_TO[:,0], 'ro', linewidth=1, markersize=1,legend="x_TO") + ax1.plot(timesteps, ee_pos_TO[:,1], 'bo', linewidth=1, markersize=1,legend="y_TO") + ax1.plot(timesteps, ee_pos_RL[:,0], 'go', linewidth=1, markersize=1,legend="x_RL") + ax1.plot(timesteps, ee_pos_RL[:,1], 'ko', linewidth=1, markersize=1,legend="y_RL") + ax1.set_xlabel('Time [s]') + ax1.set_ylabel('[m]') + ax1.set_title('End-Effector Position') + ax1.set_xlim(0, timesteps[-1]) + ax1.legend() + ax1.grid(True) + + ax2 = fig.add_subplot(2, 2, 3) + col = ['ro', 'bo', 'go'] + for i in range(self.conf.nb_action): + ax2.plot(timesteps[:-1], tau[:,i], col[i], linewidth=1, markersize=1,legend='tau{}'.format(i)) + ax2.set_xlabel('Time [s]') + ax2.set_title('Controls') + ax2.legend() + ax2.grid(True) + + ax3 = fig.add_subplot(1, 2, 2) + ax3.plot(ee_pos_TO[:,0], ee_pos_TO[:,1], 'ro', linewidth=1, markersize=2,legend='TO') + ax3.plot(ee_pos_RL[:,0], ee_pos_RL[:,1], 'bo', linewidth=1, markersize=2,legend='RL') + ax3.plot([ee_pos_TO[0,0]],[ee_pos_TO[0,1]],'ro',markersize=5) + ax3.plot([ee_pos_RL[0,0]],[ee_pos_RL[0,1]],'bo',markersize=5) + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax3.add_artist(obs_plot_list[i]) + ax3.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax3.set_xlim(self.xlim) + ax3.set_ylim(self.ylim) + ax3.set_aspect('equal', 'box') + ax3.set_xlabel('X [m]') + ax3.set_ylabel('Y [m]') + ax3.set_title('Plane') + ax3.legend() + ax3.grid(True) + + fig.tight_layout() + #plt.show() + + def plot_Return(self, ep_reward_list): + ''' Plot returns (not so meaningful given that the initial state, so also the time horizon, of each episode is randomized) ''' + fig = plt.figure(figsize=(15,8)) + ax = fig.add_subplot(1, 1, 1) + ax.set_yscale('log') + ax.plot(ep_reward_list**2) + ax.set_xlabel("Episode") + ax.set_ylabel("Return") + ax.set_title("N_try = {}".format(self.N_try)) + ax.grid(True) + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/EpReturn_{}'.format(self.N_try)) + plt.close() + + def plot_Critic_Value_function(self, critic_model, n_update, sys_id, name='V'): + ''' Plot Value function as learned by the critic ''' + if sys_id == 'manipulator': + N_discretization_x = 60 + 1 + N_discretization_y = 60 + 1 + + plot_data = np.zeros(N_discretization_y*N_discretization_x)*np.nan + ee_pos = np.zeros((N_discretization_y*N_discretization_x,3))*np.nan + + for k_x in range(N_discretization_x): + for k_y in range(N_discretization_y): + ICS = self.env.reset() + ICS[-1] = 0 + ee_pos[k_x*(N_discretization_y)+k_y,:] = self.env.get_end_effector_position(ICS) + plot_data[k_x*(N_discretization_y)+k_y] = self.NN.eval(critic_model, np.array([ICS])) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot() + plt.scatter(ee_pos[:,0], ee_pos[:,1], c=plot_data, cmap=cm.coolwarm, antialiased=False) + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + plt.colorbar() + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/{}_{}'.format(self.conf.Fig_path,self.N_try,name,int(n_update))) + plt.close() + + else: + N_discretization_x = 30 + 1 + N_discretization_y = 30 + 1 + + plot_data = np.zeros((N_discretization_y,N_discretization_x))*np.nan + + ee_x = np.linspace(-15, 15, N_discretization_x) + ee_y = np.linspace(-15, 15, N_discretization_y) + + for k_y in range(N_discretization_y): + for k_x in range(N_discretization_x): + p_ee = np.array([ee_x[k_x], ee_y[k_y], 0]) + ICS, continue_flag = self.compute_ICS(p_ee, sys_id, continue_flag=0) + if continue_flag: + continue + plot_data[k_x,k_y] = self.NN.eval(critic_model, np.array([ICS])) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot() + plt.contourf(ee_x, ee_y, plot_data.T, cmap=cm.coolwarm, antialiased=False) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + plt.colorbar() + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/{}_{}'.format(self.conf.Fig_path,self.N_try,name,int(n_update))) + plt.close() + + def plot_Critic_Value_function_from_sample(self, n_update, NSTEPS_SH, state_arr, reward_arr): + # Store transition after computing the (partial) cost-to go when using n-step TD (from 0 to Monte Carlo) + reward_to_go_arr = np.zeros(sum(NSTEPS_SH)+len(NSTEPS_SH)*1) + idx = 0 + for n in range(len(NSTEPS_SH)): + for i in range(NSTEPS_SH[n]+1): + # Compute the partial cost to go + reward_to_go_arr[idx] = sum(reward_arr[n][i:]) + idx += 1 + + state_arr = np.concatenate(state_arr, axis=0) + ee_pos_arr = np.zeros((len(state_arr),3)) + for i in range(state_arr.shape[0]): + ee_pos_arr[i,:] = self.env.get_end_effector_position(state_arr[i]) + + + mi = min(reward_to_go_arr) + ma = max(reward_to_go_arr) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot()#projection='3d') + norm = colors.Normalize(vmin=mi,vmax=ma) + + obs_plot_list = self.plot_obstaces(a=0.5) + + ax.scatter(ee_pos_arr[:,0],ee_pos_arr[:,1], c=reward_to_go_arr, norm=norm, cmap=cm.coolwarm, marker='x') + + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + + plt.colorbar(cm.ScalarMappable(norm=norm,cmap=cm.coolwarm)) + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/V_sample_{}'.format(self.conf.Fig_path,self.N_try,int(n_update))) + plt.close() + + def plot_ICS(self,state_arr): + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + for j in range(len(state_arr)): + ax.scatter(state_arr[j][0,0],state_arr[j][0,1]) + obs_plot_list = plot_fun.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.fig_ax_lim[0].tolist()) + ax.set_ylim(self.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/ICS_{}_S{}'.format(conf.Fig_path,N_try,update_step_counter,int(w_S))) + plt.close(fig) + + def plot_rollout_and_traj_from_ICS(self, init_state, n_update, actor_model, TrOp, tag, steps=200): + ''' Plot results from TO and episode to check consistency ''' + colors = cm.coolwarm(np.linspace(0.1,1,len(init_state))) + + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + + for j in range(len(init_state)): + + ee_pos_TO = np.zeros((steps,3)) + ee_pos_RL = np.zeros((steps,3)) + + RL_states = np.zeros((steps,self.conf.nb_state)) + RL_action = np.zeros((steps-1,self.conf.nb_action)) + RL_states[0,:] = init_state[j,:] + ee_pos_RL[0,:] = self.env.get_end_effector_position(RL_states[0,:]) + + for i in range(steps-1): + RL_action[i,:] = self.NN.eval(actor_model, np.array([RL_states[i,:]])) + RL_states[i+1,:] = self.env.simulate(RL_states[i,:], RL_action[i,:]) + ee_pos_RL[i+1,:] = self.env.get_end_effector_position(RL_states[i+1,:]) + + TO_states, _ = TrOp.TO_System_Solve3(init_state[j,:], RL_states.T, RL_action.T, steps-1) + + try: + for i in range(steps): + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[i,:]) + except: + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[0,:]) + + ax.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax.scatter(ee_pos_TO[0,0],ee_pos_TO[0,1],color=colors[j]) + ax.scatter(ee_pos_RL[0,0],ee_pos_RL[0,1],color=colors[j]) + ax.plot(ee_pos_TO[1:,0],ee_pos_TO[1:,1],color=colors[j]) + ax.plot(ee_pos_RL[1:,0],ee_pos_RL[1:,1],'--',color=colors[j]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.set_title('Plane') + #ax.legend() + ax.grid(True) + + plt.savefig('{}/N_try_{}/ee_traj_{}_{}'.format(self.conf.Fig_path,self.N_try,int(n_update), tag)) + + def plot_ICS(self, input_arr, cs=0): + if cs == 1: + p_arr = np.zeros((len(input_arr),3)) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + for j in range(len(input_arr)): + p_arr[j,:] = input_arr[j,:] + ax.scatter(p_arr[:,0],p_arr[:,1]) + obs_plot_list = self.plot_obstaces(a = 0.5) + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.conf.fig_ax_lim[0].tolist()) + ax.set_ylim(self.conf.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + ax.grid() + plt.savefig('{}/N_try_{}/ICS'.format(self.conf.Fig_path,self.N_try)) + plt.close(fig) + else: + p_arr = np.zeros((len(input_arr),3)) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + + for j in range(len(input_arr)): + p_arr[j,:] = self.env.get_end_effector_position(input_arr[j]) + ax.scatter(p_arr[:,0],p_arr[:,1]) + obs_plot_list = self.plot_obstaces(a = 0.5) + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.conf.fig_ax_lim[0].tolist()) + ax.set_ylim(self.conf.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + ax.grid() + plt.savefig('{}/N_try_{}/ICS'.format(self.conf.Fig_path,self.N_try)) + plt.close(fig) + + def plot_traj_from_ICS(self, init_state, TrOp, RLAC, update_step_counter=0,ep=0,steps=200, init=0,continue_flag=1): + ''' Plot results from TO and episode to check consistency ''' + colors = cm.coolwarm(np.linspace(0.1,1,len(init_state))) + + fig = plt.figure(figsize=(12,8)) + ax1 = fig.add_subplot(1,2,1) + ax2 = fig.add_subplot(1,2,2) + + for j in range(len(init_state)): + + ee_pos_TO = np.zeros((steps,3)) + ee_pos_RL = np.zeros((steps,3)) + + if init == 0: + # zeros + _, init_TO_states, init_TO_controls, _, success_init_flag = RLAC.create_TO_init(0, init_state[j,:]) + elif init == 1: + # NN + _, init_TO_states, init_TO_controls, _, success_init_flag = RLAC.create_TO_init(1, init_state[j,:]) + + if success_init_flag: + _, _, TO_states, _, _, _ = TrOp.TO_System_Solve(init_state[j,:], init_TO_states, init_TO_controls, steps-1) + else: + continue + + try: + for i in range(steps): + ee_pos_RL[i,:] = self.env.get_end_effector_position(init_TO_states[i,:]) + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[i,:]) + except: + ee_pos_RL[i,:] = self.env.get_end_effector_position(init_TO_states[0,:]) + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[0,:]) + + ax1.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax1.scatter(ee_pos_RL[0,0],ee_pos_RL[0,1],color=colors[j]) + ax1.plot(ee_pos_RL[1:,0],ee_pos_RL[1:,1],'--',color=colors[j]) + + ax2.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax2.scatter(ee_pos_TO[0,0],ee_pos_TO[0,1],color=colors[j]) + ax2.plot(ee_pos_TO[1:,0],ee_pos_TO[1:,1],color=colors[j]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax1.add_patch(obs_plot_list[i]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax2.add_patch(obs_plot_list[i]) + + ax1.set_xlim(self.xlim) + ax1.set_ylim(self.ylim) + ax1.set_aspect('equal', 'box') + ax1.set_xlabel('X [m]') + ax1.set_ylabel('Y [m]') + ax1.set_title('Warmstart traj.') + + ax2.set_xlim(self.xlim) + ax2.set_ylim(self.ylim) + ax2.set_aspect('equal', 'box') + ax2.set_xlabel('X [m]') + #ax2.set_ylabel('Y [m]') + ax2.set_title('TO traj.') + #ax.legend() + ax1.grid(True) + ax2.grid(True) + + plt.savefig('{}/N_try_{}/ee_traj_{}_{}'.format(self.conf.Fig_path,self.N_try,init,update_step_counter)) + + + + +if __name__ == '__main__': + import os + import sys + import time + import random + import importlib + import numpy as np + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # {'0' -> show all logs, '1' -> filter out info, '2' -> filter out warnings} + import tensorflow as tf + import matplotlib.pyplot as plt + import mpl_toolkits.mplot3d.art3d as art3d + + from RL import RL_AC + from plot_utils import PLOT + from NeuralNetwork import NN + + ### Input ### + N_try = 0 + + seed = 0 + tf.random.set_seed(seed) # Set tensorflow seed + random.seed(seed) # Set random seed + + system_id = 'car_park' + + TO_method = 'casadi' + + recover_training_flag = 0 + + CPU_flag = 0 + if CPU_flag: + os.environ["CUDA_VISIBLE_DEVICES"]="-1" + tf.config.experimental.list_physical_devices('GPU') + + nb_cpus = 1 + + w_S = 0 + ################################# + + # Import configuration file and environment file + system_map = { + 'single_integrator': ('conf_single_integrator', 'SingleIntegrator'), + 'double_integrator': ('conf_double_integrator', 'DoubleIntegrator'), + 'car': ('conf_car', 'Car'), + 'car_park': ('conf_car_park', 'CarPark'), + 'manipulator': ('conf_manipulator', 'Manipulator'), + 'ur5': ('conf_ur5', 'UR5') + } + + try: + conf_module, env_class = system_map[system_id] + conf = importlib.import_module(conf_module) + Environment = getattr(importlib.import_module('environment'), env_class) + except KeyError: + print('System {} not found'.format(system_id)) + sys.exit() + + + + # Create folders to store the results and the trained NNs and save configuration + for path in conf.path_list: + os.makedirs(path + '/N_try_{}'.format(N_try), exist_ok=True) + os.makedirs(conf.Config_path, exist_ok=True) + + params = [p for p in conf.__dict__ if not p.startswith("__")] + with open(conf.Config_path + '/config{}.txt'.format(N_try), 'w') as f: + for p in params: + f.write('{} = {}\n'.format(p, conf.__dict__[p])) + f.write('Seed = {}\n'.format(seed)) + f.write('w_S = {}'.format(w_S)) + + + + # Create environment instances + env = Environment(conf) + + # Create NN instance + NN_inst = NN(env, conf, w_S) + + # Create RL_AC instance + RLAC = RL_AC(env, NN_inst, conf, N_try) + + # Set initial weights of the NNs, initialize the counter of the updates and setup NN models + if recover_training_flag: + recover_training = np.array([conf.NNs_path_rec, conf.N_try_rec, conf.update_step_counter_rec]) + update_step_counter = conf.update_step_counter_rec + nb_starting_episode = (conf.update_step_counter_rec/conf.UPDATE_LOOPS)+1 + + RLAC.setup_model(recover_training) + else: + update_step_counter = 0 + nb_starting_episode = 0 + + RLAC.setup_model() + + # Create PLOT instance + plot_fun = PLOT(N_try, env, NN_inst, conf) + + plot_fun.plot_Reward() \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_0/replay_buffer.py b/Results Double Integrator/Results set test/Code/N_try_0/replay_buffer.py new file mode 100644 index 0000000..1cbf4b6 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/replay_buffer.py @@ -0,0 +1,240 @@ +import math +import random +import numpy as np +import tensorflow as tf +import segment_tree +#from stable_baselines.common.segment_tree import SumSegmentTree, MinSegmentTree + + +class ReplayBuffer(object): + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param BATCH_SIZE : (int) Size of the mini-batch + :param nb_state : (int) State size (robot state size + 1) + ''' + + self.conf = conf + self.storage_mat = np.zeros((conf.REPLAY_SIZE, conf.nb_state + 1 + conf.nb_state + conf.nb_state + 1 + 1)) + self.next_idx = 0 + self.full = 0 + self.exp_counter = np.zeros(conf.REPLAY_SIZE) + + def add(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Add transitions to the buffer ''' + data = self.concatenate_sample(obses_t, rewards, obses_t1, dVdxs, dones, terms) + + if len(data) + self.next_idx > self.conf.REPLAY_SIZE: + self.storage_mat[self.next_idx:,:] = data[:self.conf.REPLAY_SIZE-self.next_idx,:] + self.storage_mat[:self.next_idx+len(data)-self.conf.REPLAY_SIZE,:] = data[self.conf.REPLAY_SIZE-self.next_idx:,:] + self.full = 1 + else: + self.storage_mat[self.next_idx:self.next_idx+len(data),:] = data + + self.next_idx = (self.next_idx + len(data)) % self.conf.REPLAY_SIZE + + def sample(self): + ''' Sample a batch of transitions ''' + # Select indexes of the batch elements + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + idxes = np.random.randint(0, max_idx, size=self.conf.BATCH_SIZE) + + obses_t = self.storage_mat[idxes, :self.conf.nb_state] + rewards = self.storage_mat[idxes, self.conf.nb_state:self.conf.nb_state+1] + obses_t1 = self.storage_mat[idxes, self.conf.nb_state+1:self.conf.nb_state*2+1] + dVdxs = self.storage_mat[idxes, self.conf.nb_state*2+1:self.conf.nb_state*3+1] + dones = self.storage_mat[idxes, self.conf.nb_state*3+1:self.conf.nb_state*3+2] + terms = self.storage_mat[idxes, self.conf.nb_state*3+2:self.conf.nb_state*3+3] + + # Priorities not used + weights = np.ones((self.conf.BATCH_SIZE,1)) + batch_idxes = None + + # Convert the sample in tensor + obses_t, rewards, obses_t1, dVdxs, dones, weights = self.convert_sample_to_tensor(obses_t, rewards, obses_t1, dVdxs, dones, weights) + + return obses_t, rewards, obses_t1, dVdxs, dones, terms, weights, batch_idxes + + def concatenate_sample(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Convert batch of transitions into a tensor ''' + obses_t = np.concatenate(obses_t, axis=0) + rewards = np.concatenate(rewards, axis=0) + obses_t1 = np.concatenate(obses_t1, axis=0) + dVdxs = np.concatenate(dVdxs, axis=0) + dones = np.concatenate(dones, axis=0) + terms = np.concatenate(terms, axis=0) + + return np.concatenate((obses_t, rewards.reshape(-1,1), obses_t1, dVdxs, dones.reshape(-1,1), terms.reshape(-1,1)),axis=1) + + def convert_sample_to_tensor(self, obses_t, rewards, obses_t1, dVdxs, dones, weights): + ''' Convert batch of transitions into a tensor ''' + obses_t = tf.convert_to_tensor(obses_t, dtype=tf.float32) + rewards = tf.convert_to_tensor(rewards, dtype=tf.float32) + obses_t1 = tf.convert_to_tensor(obses_t1, dtype=tf.float32) + dVdxs = tf.convert_to_tensor(dVdxs, dtype=tf.float32) + dones = tf.convert_to_tensor(dones, dtype=tf.float32) + weights = tf.convert_to_tensor(weights, dtype=tf.float32) + + return obses_t, rewards, obses_t1, dVdxs, dones, weights + + + +class PrioritizedReplayBuffer: + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param BATCH_SIZE : (int) Size of the mini-batch + :param nb_state : (int) State size (robot state size + 1) + :param prioritized_replay_alpha : (float) Determines how much prioritization is used, set to 0 to use a normal buffer + :param prioritized_replay_beta : (float) Small positive constant that prevents the edge-case of transitions not being revisited once their error is zero + ''' + + self.conf = conf + + self.storage_mat = np.zeros((conf.REPLAY_SIZE, conf.nb_state + 1 + conf.nb_state + conf.nb_state + 1 + 1)) + self.full = 0 + self.next_idx = 0 + self.exp_counter = np.zeros(self.conf.REPLAY_SIZE) + self.priorities = np.empty(self.conf.REPLAY_SIZE) + + assert conf.prioritized_replay_alpha >= 0 + assert conf.prioritized_replay_beta > 0 + + it_capacity = 1 + while it_capacity < self.conf.REPLAY_SIZE: + it_capacity *= 2 + + self._it_sum = SumSegmentTree(it_capacity) + self._it_min = MinSegmentTree(it_capacity) + self._max_priority = 1.0 + + #self.RB_type = 'ReLO' + + self.MSE = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE) + + def add(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Add transitions to the buffer ''' + data = self.concatenate_sample(obses_t, rewards, obses_t1, dVdxs, dones, terms) + + if len(data) + self.next_idx > self.conf.REPLAY_SIZE: + self.storage_mat[self.next_idx:,:] = data[:self.conf.REPLAY_SIZE-self.next_idx,:] + self.storage_mat[:self.next_idx+len(data)-self.conf.REPLAY_SIZE,:] = data[self.conf.REPLAY_SIZE-self.next_idx:,:] + self.full = 1 + else: + self.storage_mat[self.next_idx:self.next_idx+len(data),:] = data + + for i in range(len(data)): + self._it_sum[(self.next_idx+i) % self.conf.REPLAY_SIZE] = self._max_priority ** self.conf.prioritized_replay_alpha + self._it_min[(self.next_idx+i) % self.conf.REPLAY_SIZE] = self._max_priority ** self.conf.prioritized_replay_alpha + + self.next_idx = (self.next_idx + len(data)) % self.conf.REPLAY_SIZE + + def _sample_proportional(self): + ''' Sample a batch of transitions ''' + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + + idx_arr = np.zeros(self.conf.BATCH_SIZE) + + p_total = self._it_sum.sum(0, max_idx - 1) + + segment = p_total / self.conf.BATCH_SIZE + + for i in range(self.conf.BATCH_SIZE): + p = random.random() * segment + i * segment #random.random(segment*i, segment*(i+1)) + idx = self._it_sum.find_prefixsum_idx(p) + idx_arr[i] = idx + + return idx_arr + + def sample(self): + ''' Sample a batch of transitions ''' + # Select indexes of the batch elements + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + + idxes = self._sample_proportional() + batch_idxes = idxes.astype(int) + + # Compute weights normalization + p_min = self._it_min.min() / self._it_sum.sum() + max_weight = (p_min * max_idx) ** (-self.conf.prioritized_replay_beta) + + self.exp_counter[batch_idxes] += 1 + self.priorities[batch_idxes] = self._it_sum[batch_idxes] / self._it_sum.sum() + weights = (self.priorities[batch_idxes] * max_idx) ** (-self.conf.prioritized_replay_beta) / max_weight + + obses_t = self.storage_mat[batch_idxes, :self.conf.nb_state] + rewards = self.storage_mat[batch_idxes, self.conf.nb_state:self.conf.nb_state+1] + obses_t1 = self.storage_mat[batch_idxes, self.conf.nb_state+1:self.conf.nb_state*2+1] + dVdxs = self.storage_mat[batch_idxes, self.conf.nb_state*2+1:self.conf.nb_state*3+1] + dones = self.storage_mat[batch_idxes, self.conf.nb_state*3+1:self.conf.nb_state*3+2] + terms = self.storage_mat[batch_idxes, self.conf.nb_state*3+2:self.conf.nb_state*3+3] + + # Convert the sample in tensor + obses_t, rewards, obses_t1, dVdxs, dones, weights = self.convert_sample_to_tensor(obses_t, rewards, obses_t1, dVdxs, dones, weights) + + return obses_t, rewards, obses_t1, dVdxs, dones, terms, weights, batch_idxes + + def update_priorities(self, idxes, reward_to_go_batch, critic_value, target_critic_value=None): + '''Update priorities of sampled transitions ''' + # Create td_errors + if self.RB_type == 'ReLO': + # MSE(Delta V_c) - MSE(Delta V_t) + td_errors = self.MSE(reward_to_go_batch, critic_value).numpy()-self.MSE(reward_to_go_batch, target_critic_value).numpy() + td_errors_norm = np.clip(td_errors, 0, np.max(td_errors)) + else: # 'PER' is default self.RB_type + # |TD_error_i| + td_errors_norm = tf.math.abs(tf.math.subtract(reward_to_go_batch, critic_value)) + td_errors_norm = td_errors_norm[:,0] + + # Compute the freshness discount factor + fresh_disc_factor = self.conf.fresh_factor**self.exp_counter[idxes] + + # Compute new priorities: p_i = mu**C_i * td_error + self.conf.prioritized_replay_eps + new_priorities = fresh_disc_factor * td_errors_norm + self.conf.prioritized_replay_eps + + # Sets priority of transition at index idxes[i] in buffer to priorities[i] + assert len(idxes) == len(new_priorities) + for idx, priority in zip(idxes, new_priorities): + assert priority > 0 + + idx = int(idx) + + self._it_sum[idx] = priority ** self.conf.prioritized_replay_alpha + self._it_min[idx] = priority ** self.conf.prioritized_replay_alpha + + self._max_priority = max(self._max_priority, priority) + + def concatenate_sample(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Convert batch of transitions into a tensor ''' + obses_t = np.concatenate(obses_t, axis=0) + rewards = np.concatenate(rewards, axis=0) + obses_t1 = np.concatenate(obses_t1, axis=0) + dVdxs = np.concatenate(dVdxs, axis=0) + dones = np.concatenate(dones, axis=0) + terms = np.concatenate(terms, axis=0) + + return np.concatenate((obses_t, rewards.reshape(-1,1), obses_t1, dVdxs, dones.reshape(-1,1), terms.reshape(-1,1)),axis=1) + + def convert_sample_to_tensor(self, obses_t, rewards, obses_t1, dVdxs, dones, weights): + ''' Convert batch of transitions into a tensor ''' + obses_t = tf.convert_to_tensor(obses_t, dtype=tf.float32) + rewards = tf.convert_to_tensor(rewards, dtype=tf.float32) + obses_t1 = tf.convert_to_tensor(obses_t1, dtype=tf.float32) + dVdxs = tf.convert_to_tensor(dVdxs, dtype=tf.float32) + dones = tf.convert_to_tensor(dones, dtype=tf.float32) + weights = tf.convert_to_tensor(weights, dtype=tf.float32) + + return obses_t, rewards, obses_t1, dVdxs, dones, weights \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_0/robot_utils.py b/Results Double Integrator/Results set test/Code/N_try_0/robot_utils.py new file mode 100644 index 0000000..d3cc937 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/robot_utils.py @@ -0,0 +1,637 @@ +import os +import math +import random +#import gepetto#.corbaserver +import time +import subprocess +import numpy as np +import pinocchio as pin +from numpy.linalg import norm +from pinocchio.robot_wrapper import RobotWrapper as PinocchioRobotWrapper + +class Contact: + ''' A contact between a contact-point and a contact-surface + ''' + def __init__(self, contact_point, contact_surface): + self.cp = contact_point + self.cs = contact_surface + self.reset_contact_position() + + def reset_contact_position(self): + # Initialize anchor point p0, that is the initial (0-load) position of the spring + self.p0 = self.cp.get_position() + self.in_contact = True + + def compute_force(self): + self.f, self.p0 = self.cs.compute_force(self.cp, self.p0) + return self.f + + def get_jacobian(self): + return self.cp.get_jacobian() + +class RobotWrapper(PinocchioRobotWrapper): + + @staticmethod + def BuildFromURDF(filename, package_dirs=None, root_joint=None, verbose=False, meshLoader=None): + robot = RobotWrapper() + robot.initFromURDF(filename, package_dirs, root_joint, verbose, meshLoader) + return robot + + @property + def na(self): + if(self.model.joints[0].nq==7): + return self.model.nv-6 + return self.model.nv + + def mass(self, q, update=True): + if(update): + return pin.crba(self.model, self.data, q) + return self.data.M + + def nle(self, q, v, update=True): + if(update): + return pin.nonLinearEffects(self.model, self.data, q, v) + return self.data.nle + + def com(self, q=None, v=None, a=None, update=True): + if(update==False or q is None): + return PinocchioRobotWrapper.com(self, q) + if a is None: + if v is None: + return PinocchioRobotWrapper.com(self, q) + return PinocchioRobotWrapper.com(self, q, v) + return PinocchioRobotWrapper.com(self, q, v,a) + + def Jcom(self, q, update=True): + if(update): + return pin.jacobianCenterOfMass(self.model, self.data, q) + return self.data.Jcom + + def momentumJacobian(self, q, v, update=True): + if(update): + pin.ccrba(self.model, self.data, q, v) + return self.data.Ag + + def computeAllTerms(self, q, v): + ''' pin.computeAllTerms is equivalent to calling: + pinocchio::forwardKinematics + pinocchio::crba + pinocchio::nonLinearEffects + pinocchio::computeJointJacobians + pinocchio::centerOfMass + pinocchio::jacobianCenterOfMass + pinocchio::kineticEnergy + pinocchio::potentialEnergy + This is too much for our needs, so we call only the functions + we need, including those for the frame kinematics + ''' + #pin.computeAllTerms(self.model, self.data, q, v); + pin.forwardKinematics(self.model, self.data, q, v, np.zeros(self.model.nv)) + pin.computeJointJacobians(self.model, self.data) + pin.updateFramePlacements(self.model, self.data) + pin.crba(self.model, self.data, q) + pin.nonLinearEffects(self.model, self.data, q, v) + + def forwardKinematics(self, q, v=None, a=None): + if v is not None: + if a is not None: + pin.forwardKinematics(self.model, self.data, q, v, a) + else: + pin.forwardKinematics(self.model, self.data, q, v) + else: + pin.forwardKinematics(self.model, self.data, q) + + def inverseKinematics(self, ee_coords): + ''' Inverse kinematics algorithm to compute a joint configuration given the EE coordinates''' + + oMdes = pin.SE3(np.eye(3), np.array([ee_coords[0], ee_coords[1], 0.0])) + random.seed(123) + for j in range(100): + q = np.array([random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi)]) + # q = pin.neutral(self.model) + + pin.forwardKinematics(self.model, self.data, q) + pin.updateFramePlacements(self.model, self.data) + eps = 1e-4 + IT_MAX = 1000 + DT = 1e-1 + damp = 1e-12 + frame_id = self.model.getFrameId('fixed_ee') + i=0 + while True: + pin.forwardKinematics(self.model,self.data,q) + pin.updateFramePlacements(self.model, self.data) + dMi = oMdes.actInv(self.data.oMf[frame_id]) + err = pin.log(dMi).vector + if norm(err) < eps: + success = True + break + if i >= IT_MAX: + success = False + break + J = pin.computeFrameJacobian(self.model,self.data,q,frame_id) + v = - J.T.dot(np.linalg.solve(J.dot(J.T) + damp * np.eye(6), err)) + q = pin.integrate(self.model,q,v*DT) + if not i % 10: + print('%d: error = %s' % (i, err.T)) + i += 1 + + if success: + print("Convergence achieved!") + break + else: + print("\nWarning: the iterative algorithm has not reached convergence to the desired precision. Retry ({}) with another initial configuration".format(j)) + + return q, success + + def frameJacobian(self, q, index, update=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + ''' Call computeFrameJacobian if update is true. If not, user should call computeFrameJacobian first. + Then call getFrameJacobian and return the Jacobian matrix. + ref_frame can be: ReferenceFrame.LOCAL, ReferenceFrame.WORLD, ReferenceFrame.LOCAL_WORLD_ALIGNED + ''' + if(update): + pin.computeFrameJacobian(self.model, self.data, q, index) + return pin.getFrameJacobian(self.model, self.data, index, ref_frame) + + def frameVelocity(self, q, v, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v) + v_local = pin.getFrameVelocity(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return v_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + v_world = H.act(v_local) + return v_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + v = Hr.act(v_local) + return v + + def frameAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def frameClassicAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + v = pin.getFrameVelocity(self.model, self.data, index) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + a_local.linear += np.cross(v.angular, v.linear, axis=0) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def deactivateCollisionPairs(self, collision_pair_indexes): + for i in collision_pair_indexes: + self.collision_data.deactivateCollisionPair(i) + + def addAllCollisionPairs(self): + self.collision_model.addAllCollisionPairs() + self.collision_data = pin.GeometryData(self.collision_model) + + def isInCollision(self, q, stop_at_first_collision=True): + return pin.computeCollisions(self.model, self.data, self.collision_model, self.collision_data, np.asmatrix(q).reshape((self.model.nq,1)), stop_at_first_collision) + + def findFirstCollisionPair(self, consider_only_active_collision_pairs=True): + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + return (i, self.collision_model.collisionPairs[i]) + return None + + def findAllCollisionPairs(self, consider_only_active_collision_pairs=True): + res = [] + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + res += [(i, self.collision_model.collisionPairs[i])] + return res + +class RobotSimulator: + + def __init__(self, robot, q0_init, v0_init, simulation_type, tau_coulomb_max, use_viewer=False, DISPLAY_T=None, CAMERA_TRANSFORM=None, show_floor=False): + self.robot = robot + self.model = self.robot.model + self.data = self.model.createData() + self.t = 0.0 # time + self.nv = nv = self.model.nv # Dimension of joint velocities vector + self.na = na = robot.na # number of actuated joints + self.S = np.hstack((np.zeros((na, nv-na)), np.eye(na, na))) # Matrix S used as filter of vector of inputs U + + self.contacts = [] + self.candidate_contact_points = [] # candidate contact points + self.contact_surfaces = [] + + self.DISPLAY_T = DISPLAY_T # refresh period for viewer + self.use_viewer = use_viewer + self.tau_coulomb_max = tau_coulomb_max + self.display_counter = self.DISPLAY_T + self.init(q0_init, v0_init, True) + + self.tau_c = np.zeros(na) # Coulomb friction torque + self.simulation_type = simulation_type + self.set_coulomb_friction(tau_coulomb_max) + + # for gepetto viewer + self.gui = None + if(use_viewer): + try: + prompt = subprocess.getstatusoutput("ps aux |grep 'gepetto-gui'|grep -v 'grep'|wc -l") + if int(prompt[1]) == 0: + os.system('gepetto-gui &') + time.sleep(1) + except: + pass + gepetto.corbaserver.Client() + gepetto.Client() + self.robot.initViewer(loadModel=True) + self.gui = self.robot.viewer.gui + if(show_floor): + self.robot.viewer.gui.createSceneWithFloor('world') + self.gui.setLightingMode('world/floor', 'OFF') + self.robot.displayCollisions(False) + self.robot.displayVisuals(True) + self.robot.display(self.q) + try: + self.gui.setCameraTransform("python-pinocchio", CAMERA_TRANSFORM) + except: + self.gui.setCameraTransform(0, CAMERA_TRANSFORM) + + # Re-initialize the simulator + def init(self, q0=None, v0=None, reset_contact_positions=False): + self.first_iter = True + + if q0 is not None: + self.q = q0.copy() + + if(v0 is None): + self.v = np.zeros(self.robot.nv) + else: + self.v = v0.copy() + self.dv = np.zeros(self.robot.nv) + self.resize_contact_data(reset_contact_positions) + + def resize_contact_data(self, reset_contact_positions=False): + self.nc = len(self.contacts) # number of contacts + self.nk = 3*self.nc # size of contact force vector + self.f = np.zeros(self.nk) # contact forces + self.Jc = np.zeros((self.nk, self.model.nv)) # contact Jacobian + + # reset contact position + if(reset_contact_positions): + pin.forwardKinematics(self.model, self.data, self.q) + pin.updateFramePlacements(self.model, self.data) + for c in self.contacts: + c.reset_contact_position() + + self.compute_forces(compute_data=True) + + def set_coulomb_friction(self, tau_max): + self.tau_coulomb_max = 1e-2*tau_max*self.model.effortLimit + self.simulate_coulomb_friction = (norm(self.tau_coulomb_max)!=0.0) + + def collision_detection(self): + for s in self.contact_surfaces: # for each contact surface + for cp in self.candidate_contact_points: # for each candidate contact point + p = cp.get_position() + if(s.check_collision(p)): # check whether the point is colliding with the surface + if(not cp.active): # if the contact was not already active + print("Collision detected between point", cp.frame_name, " at ", p) + cp.active = True + cp.contact = Contact(cp, s) + self.contacts += [cp.contact] + self.resize_contact_data() + else: + if(cp.active): + print("Contact lost between point", cp.frame_name, " at ", p) + cp.active = False + self.contacts.remove(cp.contact) + self.resize_contact_data() + + def compute_forces(self, compute_data=True): + '''Compute the contact forces from q, v and elastic model''' + if compute_data: + pin.forwardKinematics(self.model, self.data, self.q, self.v) + # Computes the placements of all the operational frames according to the current joint placement stored in data + pin.updateFramePlacements(self.model, self.data) + self.collision_detection() + + i = 0 + for c in self.contacts: + self.f[i:i+3] = c.compute_force() + self.Jc[i:i+3, :] = c.get_jacobian() + i += 3 + return self.f + + def step(self, u, dt=None): + if dt is None: + dt = self.dt + + # (Forces are directly in the world frame, and aba wants them in the end effector frame) + pin.computeAllTerms(self.model, self.data, self.q, self.v) + pin.updateFramePlacements(self.model, self.data) + M = self.data.M + h = self.data.nle + self.collision_detection() + self.compute_forces(False) + + if(self.simulate_coulomb_friction and self.simulation_type=='timestepping'): + # minimize kinetic energy using time stepping + from quadprog import solve_qp + ''' + Solve a strictly convex quadratic program + + Minimize 1/2 x^T G x - a^T x + Subject to C.T x >= b + + Input Parameters + ---------- + G : array, shape=(n, n) + a : array, shape=(n,) + C : array, shape=(n, m) matrix defining the constraints + b : array, shape=(m), default=None, vector defining the constraints + meq : int, default=0 + the first meq constraints are treated as equality constraints, + all further as inequality constraints + ''' + # M (v' - v) = dt*S^T*(tau - tau_c) - dt*h + dt*J^T*f + # M v' = M*v + dt*(S^T*tau - h + J^T*f) - dt*S^T*tau_c + # M v' = b + B*tau_c + # v' = Minv*(b + B*tau_c) + b = M.dot(self.v) + dt*(self.S.T.dot(u) - h + self.Jc.T.dot(self.f)) + B = - dt*self.S.T + # Minimize kinetic energy: + # min v'.T * M * v' + # min (b+B*tau_c​).T*Minv*(b+B*tau_c​) + # min tau_c.T * B.T*Minv*B* tau_C + 2*b.T*Minv*B*tau_c + Minv = np.linalg.inv(M) + G = B.T.dot(Minv.dot(B)) + a = -b.T.dot(Minv.dot(B)) + C = np.vstack((np.eye(self.na), -np.eye(self.na))) + c = np.concatenate((-self.tau_coulomb_max, -self.tau_coulomb_max)) + solution = solve_qp(G, a, C.T, c, 0) + self.tau_c = solution[0] + self.v = Minv.dot(b + B.dot(self.tau_c)) + self.q = pin.integrate(self.model, self.q, self.v*dt) + self.dv = np.linalg.solve(M, self.S.T.dot(u-self.tau_c) - h + self.Jc.T.dot(self.f)) + elif(self.simulation_type=='euler' or self.simulate_coulomb_friction==False): + self.tau_c = self.tau_coulomb_max*np.sign(self.v[-self.na:]) + self.dv = np.linalg.solve(M, self.S.T.dot(u-self.tau_c) - h + self.Jc.T.dot(self.f)) + # v_mean = np.copy(self.v) + 0.5*dt*self.dv + self.q = pin.integrate(self.model, self.q, self.v*dt) + # self.q += dt*v_mean + self.v += self.dv*dt + else: + print("[ERROR] Unknown simulation type:", self.simulation_type) + + self.t += dt + return self.q, self.v, self.dv + + def reset(self): + self.first_iter = True + + def simulate(self, state, u, dt=0.05, ndt=1): + ''' Perform ndt steps, each lasting dt/ndt seconds ''' + self.q = state[:self.robot.nq] + self.v = state[self.robot.nq:self.robot.nv+self.robot.nq] + + tau_c_avg = 0*self.tau_c + for i in range(ndt): + self.q, self.v, self.dv = self.step(u, dt/ndt) + tau_c_avg += self.tau_c + self.tau_c = tau_c_avg / ndt + + if (self.use_viewer): + self.display_counter -= dt + if self.display_counter <= 0.0: + self.robot.display(self.q) + self.display_counter = self.DISPLAY_T + + return self.q, self.v, self.f + + def display(self, q): + if(self.use_viewer): + self.robot.display(q) + + +class RobotWrapper(PinocchioRobotWrapper): + + @staticmethod + def BuildFromURDF(filename, package_dirs=None, root_joint=None, verbose=False, meshLoader=None): + robot = RobotWrapper() + robot.initFromURDF(filename, package_dirs, root_joint, verbose, meshLoader) + return robot + + @property + def na(self): + if(self.model.joints[0].nq==7): + return self.model.nv-6 + return self.model.nv + + def mass(self, q, update=True): + if(update): + return pin.crba(self.model, self.data, q) + return self.data.M + + def nle(self, q, v, update=True): + if(update): + return pin.nonLinearEffects(self.model, self.data, q, v) + return self.data.nle + + def com(self, q=None, v=None, a=None, update=True): + if(update==False or q is None): + return PinocchioRobotWrapper.com(self, q) + if a is None: + if v is None: + return PinocchioRobotWrapper.com(self, q) + return PinocchioRobotWrapper.com(self, q, v) + return PinocchioRobotWrapper.com(self, q, v,a) + + def Jcom(self, q, update=True): + if(update): + return pin.jacobianCenterOfMass(self.model, self.data, q) + return self.data.Jcom + + def momentumJacobian(self, q, v, update=True): + if(update): + pin.ccrba(self.model, self.data, q, v) + return self.data.Ag + + def computeAllTerms(self, q, v): + ''' pin.computeAllTerms is equivalent to calling: + pinocchio::forwardKinematics + pinocchio::crba + pinocchio::nonLinearEffects + pinocchio::computeJointJacobians + pinocchio::centerOfMass + pinocchio::jacobianCenterOfMass + pinocchio::kineticEnergy + pinocchio::potentialEnergy + This is too much for our needs, so we call only the functions + we need, including those for the frame kinematics + ''' + #pin.computeAllTerms(self.model, self.data, q, v); + pin.forwardKinematics(self.model, self.data, q, v, np.zeros(self.model.nv)) + pin.computeJointJacobians(self.model, self.data) + pin.updateFramePlacements(self.model, self.data) + pin.crba(self.model, self.data, q) + pin.nonLinearEffects(self.model, self.data, q, v) + + def forwardKinematics(self, q, v=None, a=None): + if v is not None: + if a is not None: + pin.forwardKinematics(self.model, self.data, q, v, a) + else: + pin.forwardKinematics(self.model, self.data, q, v) + else: + pin.forwardKinematics(self.model, self.data, q) + + def inverseKinematics(self, ee_coords): + ''' Inverse kinematics algorithm to compute a joint configuration given the EE coordinates''' + + oMdes = pin.SE3(np.eye(3), np.array([ee_coords[0], ee_coords[1], 0.0])) + random.seed(123) + for j in range(100): + q = np.array([random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi)]) + # q = pin.neutral(self.model) + + pin.forwardKinematics(self.model, self.data, q) + pin.updateFramePlacements(self.model, self.data) + eps = 1e-4 + IT_MAX = 1000 + DT = 1e-1 + damp = 1e-12 + frame_id = self.model.getFrameId('fixed_ee') + i=0 + while True: + pin.forwardKinematics(self.model,self.data,q) + pin.updateFramePlacements(self.model, self.data) + dMi = oMdes.actInv(self.data.oMf[frame_id]) + err = pin.log(dMi).vector + if norm(err) < eps: + success = True + break + if i >= IT_MAX: + success = False + break + J = pin.computeFrameJacobian(self.model,self.data,q,frame_id) + v = - J.T.dot(np.linalg.solve(J.dot(J.T) + damp * np.eye(6), err)) + q = pin.integrate(self.model,q,v*DT) + if not i % 10: + print('%d: error = %s' % (i, err.T)) + i += 1 + + if success: + print("Convergence achieved!") + break + else: + print("\nWarning: the iterative algorithm has not reached convergence to the desired precision. Retry ({}) with another initial configuration".format(j)) + + return q, success + + def frameJacobian(self, q, index, update=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + ''' Call computeFrameJacobian if update is true. If not, user should call computeFrameJacobian first. + Then call getFrameJacobian and return the Jacobian matrix. + ref_frame can be: ReferenceFrame.LOCAL, ReferenceFrame.WORLD, ReferenceFrame.LOCAL_WORLD_ALIGNED + ''' + if(update): + pin.computeFrameJacobian(self.model, self.data, q, index) + return pin.getFrameJacobian(self.model, self.data, index, ref_frame) + + def frameVelocity(self, q, v, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v) + v_local = pin.getFrameVelocity(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return v_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + v_world = H.act(v_local) + return v_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + v = Hr.act(v_local) + return v + + def frameAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def frameClassicAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + v = pin.getFrameVelocity(self.model, self.data, index) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + a_local.linear += np.cross(v.angular, v.linear, axis=0) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def deactivateCollisionPairs(self, collision_pair_indexes): + for i in collision_pair_indexes: + self.collision_data.deactivateCollisionPair(i) + + def addAllCollisionPairs(self): + self.collision_model.addAllCollisionPairs() + self.collision_data = pin.GeometryData(self.collision_model) + + def isInCollision(self, q, stop_at_first_collision=True): + return pin.computeCollisions(self.model, self.data, self.collision_model, self.collision_data, np.asmatrix(q).reshape((self.model.nq,1)), stop_at_first_collision) + + def findFirstCollisionPair(self, consider_only_active_collision_pairs=True): + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + return (i, self.collision_model.collisionPairs[i]) + return None + + def findAllCollisionPairs(self, consider_only_active_collision_pairs=True): + res = [] + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + res += [(i, self.collision_model.collisionPairs[i])] + return res \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_0/segment_tree.py b/Results Double Integrator/Results set test/Code/N_try_0/segment_tree.py new file mode 100644 index 0000000..cb386ec --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/segment_tree.py @@ -0,0 +1,145 @@ +import operator + + +class SegmentTree(object): + def __init__(self, capacity, operation, neutral_element): + """Build a Segment Tree data structure. + + https://en.wikipedia.org/wiki/Segment_tree + + Can be used as regular array, but with two + important differences: + + a) setting item's value is slightly slower. + It is O(lg capacity) instead of O(1). + b) user has access to an efficient ( O(log segment size) ) + `reduce` operation which reduces `operation` over + a contiguous subsequence of items in the array. + + Paramters + --------- + capacity: int + Total size of the array - must be a power of two. + operation: lambda obj, obj -> obj + and operation for combining elements (eg. sum, max) + must form a mathematical group together with the set of + possible values for array elements (i.e. be associative) + neutral_element: obj + neutral element for the operation above. eg. float('-inf') + for max and 0 for sum. + """ + assert capacity > 0 and capacity & (capacity - 1) == 0, "capacity must be positive and a power of 2." + self._capacity = capacity + self._value = [neutral_element for _ in range(2 * capacity)] + self._operation = operation + + def _reduce_helper(self, start, end, node, node_start, node_end): + if start == node_start and end == node_end: + return self._value[node] + mid = (node_start + node_end) // 2 + if end <= mid: + return self._reduce_helper(start, end, 2 * node, node_start, mid) + else: + if mid + 1 <= start: + return self._reduce_helper(start, end, 2 * node + 1, mid + 1, node_end) + else: + return self._operation( + self._reduce_helper(start, mid, 2 * node, node_start, mid), + self._reduce_helper(mid + 1, end, 2 * node + 1, mid + 1, node_end) + ) + + def reduce(self, start=0, end=None): + """Returns result of applying `self.operation` + to a contiguous subsequence of the array. + + self.operation(arr[start], operation(arr[start+1], operation(... arr[end]))) + + Parameters + ---------- + start: int + beginning of the subsequence + end: int + end of the subsequences + + Returns + ------- + reduced: obj + result of reducing self.operation over the specified range of array elements. + """ + if end is None: + end = self._capacity + if end < 0: + end += self._capacity + end -= 1 + return self._reduce_helper(start, end, 1, 0, self._capacity - 1) + + def __setitem__(self, idx, val): + # index of the leaf + idx += self._capacity + self._value[idx] = val + idx //= 2 + while idx >= 1: + self._value[idx] = self._operation( + self._value[2 * idx], + self._value[2 * idx + 1] + ) + idx //= 2 + + def __getitem__(self, idx): + assert 0 <= idx < self._capacity + return self._value[self._capacity + idx] + + +class SumSegmentTree(SegmentTree): + def __init__(self, capacity): + super(SumSegmentTree, self).__init__( + capacity=capacity, + operation=operator.add, + neutral_element=0.0 + ) + + def sum(self, start=0, end=None): + """Returns arr[start] + ... + arr[end]""" + return super(SumSegmentTree, self).reduce(start, end) + + def find_prefixsum_idx(self, prefixsum): + """Find the highest index `i` in the array such that + sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum + + if array values are probabilities, this function + allows to sample indexes according to the discrete + probability efficiently. + + Parameters + ---------- + perfixsum: float + upperbound on the sum of array prefix + + Returns + ------- + idx: int + highest index satisfying the prefixsum constraint + """ + assert 0 <= prefixsum <= self.sum() + 1e-5 + idx = 1 + while idx < self._capacity: # while non-leaf + if self._value[2 * idx] > prefixsum: + idx = 2 * idx + else: + prefixsum -= self._value[2 * idx] + idx = 2 * idx + 1 + return idx - self._capacity + + +class MinSegmentTree(SegmentTree): + def __init__(self, capacity): + super(MinSegmentTree, self).__init__( + capacity=capacity, + operation=min, + neutral_element=float('inf') + ) + + def min(self, start=0, end=None): + """Returns min(arr[start], ..., arr[end])""" + + return super(MinSegmentTree, self).reduce(start, end) diff --git a/Results Double Integrator/Results set test/Code/N_try_0/utils.py b/Results Double Integrator/Results set test/Code/N_try_0/utils.py new file mode 100644 index 0000000..c426b89 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_0/utils.py @@ -0,0 +1,42 @@ +import tensorflow as tf +import numpy as np + +def array2tensor(array): + + return tf.expand_dims(tf.convert_to_tensor(array), 0) + +def de_normalize_tensor(state, state_norm_arr): + ''' Retrieve state from normalized state - tensor ''' + state_time = tf.concat([tf.zeros([state.shape[0], state.shape[1]-1]), tf.reshape((state[:,-1]+1)*state_norm_arr[-1]/2,[state.shape[0],1])],1) + state_no_time = state * state_norm_arr + mask = tf.concat([tf.ones([state.shape[0], state.shape[1]-1]), tf.zeros([state.shape[0], 1])],1) + state_not_norm = state_no_time * mask + state_time * (1 - mask) + + return state_not_norm + +def normalize_tensor(state, state_norm_arr): + ''' Retrieve state from normalized state - tensor ''' + state_norm_time = tf.concat([tf.zeros([state.shape[0], state.shape[1]-1]), tf.reshape(((state[:,-1]) / state_norm_arr[-1])*2 - 1,[state.shape[0],1])],1) + state_norm_no_time = state / state_norm_arr + mask = tf.concat([tf.ones([state.shape[0], state.shape[1]-1]), tf.zeros([state.shape[0], 1])],1) + state_norm = state_norm_no_time * mask + state_norm_time * (1 - mask) + + return state_norm + +def de_normalize(state, state_norm_arr): + ''' Retrieve state from normalized state ''' + state_not_norm = np.empty_like(state) + state_not_norm[:-1] = state[:-1] * state_norm_arr[:-1] + state_not_norm[-1] = (state[-1] + 1) * state_norm_arr[-1]/2 + + return state_not_norm + +def normalize(state, state_norm_arr): + ''' Normalize state ''' + state_norm = np.empty_like(state) + state_norm = state / state_norm_arr + state_norm[-1] = state_norm[-1] * 2 -1 + + return state_norm + + diff --git a/Results Double Integrator/Results set test/Code/N_try_6/NeuralNetwork.py b/Results Double Integrator/Results set test/Code/N_try_6/NeuralNetwork.py new file mode 100644 index 0000000..90f4127 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/NeuralNetwork.py @@ -0,0 +1,233 @@ +import math +import random +import numpy as np +import tensorflow as tf +from tensorflow.keras import layers, regularizers +from tf_siren import SinusodialRepresentationDense + +from utils import normalize_tensor + +class NN: + + def __init__(self, env, conf, w_S=0): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :param NH1: (int) 1st hidden layer size + :param NH2: (int) 2nd hidden layer size + :param kreg_l1_A : (float) Weight of L1 regularization in actor's network - kernel + :param kreg_l2_A : (float) Weight of L2 regularization in actor's network - kernel + :param breg_l1_A : (float) Weight of L2 regularization in actor's network - bias + :param breg_l2_A : (float) Weight of L2 regularization in actor's network - bias + :param kreg_l1_C : (float) Weight of L1 regularization in critic's network - kernel + :param kreg_l2_C : (float) Weight of L2 regularization in critic's network - kernel + :param breg_l1_C : (float) Weight of L1 regularization in critic's network - bias + :param breg_l2_C : (float) Weight of L2 regularization in critic's network - bias + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param NORMALIZE_INPUTS : (bool) Flag to normalize inputs (state) + :param state_norm_array : (float array) Array used to normalize states + :param MC : (bool) Flag to use MC or TD(n) + :param cost_weights_terminal : (float array) Running cost weights vector + :param cost_weights_running : (float array) Terminal cost weights vector + :param BATCH_SIZE : (int) Size of the mini-batch + :param dt : (float) Timestep + + :input w_S : (float) Sobolev-training weight + ''' + + self.env = env + self.conf = conf + + self.w_S = w_S + + self.MSE = tf.keras.losses.MeanSquaredError() + + return + + def create_actor(self): + ''' Create actor NN ''' + inputs = layers.Input(shape=(self.conf.nb_state,)) + + lay1 = layers.Dense(self.conf.NH1,kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(inputs) + leakyrelu1 = layers.LeakyReLU()(lay1) + lay2 = layers.Dense(self.conf.NH2, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(leakyrelu1) + leakyrelu2 = layers.LeakyReLU()(lay2) + outputs = layers.Dense(self.conf.nb_action, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(leakyrelu2) + + model = tf.keras.Model(inputs, outputs) + + return model + + def create_critic_elu(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = layers.Dense(16, activation='elu')(state_input) + state_out2 = layers.Dense(32, activation='elu')(state_out1) + out_lay1 = layers.Dense(256, activation='elu')(state_out2) + out_lay2 = layers.Dense(256, activation='elu')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_sine_elu(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = SinusodialRepresentationDense(64, activation='sine')(state_input) + state_out2 = layers.Dense(64, activation='elu')(state_out1) + out_lay1 = SinusodialRepresentationDense(128, activation='sine')(state_out2) + out_lay2 = layers.Dense(128, activation='elu')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_sine(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = SinusodialRepresentationDense(64, activation='sine')(state_input) + state_out2 = SinusodialRepresentationDense(64, activation='sine')(state_out1) + out_lay1 = SinusodialRepresentationDense(128, activation='sine')(state_out2) + out_lay2 = SinusodialRepresentationDense(128, activation='sine')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_relu(self): + ''' Create critic NN - relu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = layers.Dense(16, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(state_input) + leakyrelu1 = layers.LeakyReLU()(state_out1) + + state_out2 = layers.Dense(32, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu1) + leakyrelu2 = layers.LeakyReLU()(state_out2) + out_lay1 = layers.Dense(self.conf.NH1, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu2) + leakyrelu3 = layers.LeakyReLU()(out_lay1) + out_lay2 = layers.Dense(self.conf.NH2, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu3) + leakyrelu4 = layers.LeakyReLU()(out_lay2) + + outputs = layers.Dense(1, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu4) + + model = tf.keras.Model([state_input], outputs) + + return model + + def eval(self, NN, input): + ''' Compute the output of a NN given an input ''' + if not tf.is_tensor(input): + input = tf.convert_to_tensor(input, dtype=tf.float32) + + if self.conf.NORMALIZE_INPUTS: + input = normalize_tensor(input, self.conf.state_norm_arr) + + return NN(input, training=True) + + def custom_logarithm(self,input): + # Calculate the logarithms based on the non-zero condition + positive_log = tf.math.log(tf.math.maximum(input, 1e-7) + 1) + negative_log = -tf.math.log(tf.math.maximum(-input, 1e-7) + 1) + + # Use the appropriate logarithm based on the condition + result = tf.where(input > 0, positive_log, negative_log) + + return result + + def compute_critic_grad(self, critic_model, target_critic, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, weights_batch): + ''' Compute the gradient of the critic NN ''' + with tf.GradientTape() as tape: + # Compute value function tail if TD(n) is used + if self.conf.MC: + reward_to_go_batch = partial_reward_to_go_batch + else: + target_values = self.eval(target_critic, state_next_rollout_batch) # Compute Value at next state after conf.nsteps_TD_N steps given by target critic + reward_to_go_batch = partial_reward_to_go_batch + (1-d_batch)*target_values # Compute batch of 1-step targets for the critic loss + + # Compute critic loss + if self.w_S != 0: + with tf.GradientTape() as tape2: + tape2.watch(state_batch) + critic_value = self.eval(critic_model, state_batch) + der_critic_value = tape2.gradient(critic_value, state_batch) + + critic_loss_v = self.MSE(reward_to_go_batch, critic_value, sample_weight=weights_batch) + critic_loss_der = self.MSE(self.custom_logarithm(dVdx_batch[:,:-1]), self.custom_logarithm(der_critic_value[:,:-1]), sample_weight=weights_batch) # dV/dt not computed and so not used in the update + + critic_loss = critic_loss_der + self.w_S*critic_loss_v + else: + critic_value = self.eval(critic_model, state_batch) + critic_loss = self.MSE(reward_to_go_batch, critic_value, sample_weight=weights_batch) + + # Compute the gradients of the critic loss w.r.t. critic's parameters + critic_grad = tape.gradient(critic_loss, critic_model.trainable_variables) + + return critic_grad, reward_to_go_batch, critic_value, self.eval(target_critic, state_batch) + + def compute_actor_grad(self, actor_model, critic_model, state_batch, term_batch, batch_size): + ''' Compute the gradient of the actor NN ''' + if batch_size == None: + batch_size = self.conf.BATCH_SIZE + + actions = self.eval(actor_model, state_batch) + + # Both take into account normalization, ds_next_da is the gradient of the dynamics w.r.t. policy actions (ds'_da) + state_next_tf, ds_next_da = self.env.simulate_batch(state_batch.numpy(), actions.numpy()) , self.env.derivative_batch(state_batch.numpy(), actions.numpy()) + + with tf.GradientTape() as tape: + tape.watch(state_next_tf) + critic_value_next = self.eval(critic_model,state_next_tf) + + # dV_ds' = gradient of V w.r.t. s', where s'=f(s,a) a=policy(s) + dV_ds_next = tape.gradient(critic_value_next, state_next_tf) + + cost_weights_terminal_reshaped = np.reshape(self.conf.cost_weights_terminal,[1,len(self.conf.cost_weights_terminal)]) + cost_weights_running_reshaped = np.reshape(self.conf.cost_weights_running,[1,len(self.conf.cost_weights_running)]) + with tf.GradientTape() as tape1: + tape1.watch(actions) + rewards_tf = self.env.reward_batch(term_batch.dot(cost_weights_terminal_reshaped) + (1-term_batch).dot(cost_weights_running_reshaped), state_batch.numpy(), actions) + + # dr_da = gradient of reward r(s,a) w.r.t. policy's action a + dr_da = tape1.gradient(rewards_tf, actions, unconnected_gradients=tf.UnconnectedGradients.ZERO) + + dr_da_reshaped = tf.reshape(dr_da, (batch_size, 1, self.conf.nb_action)) + + # dr_ds' + dV_ds' (note: dr_ds' = 0) + dQ_ds_next = tf.reshape(dV_ds_next, (batch_size, 1, self.conf.nb_state)) + + # (dr_ds' + dV_ds')*ds'_da + dQ_ds_next_da = tf.matmul(dQ_ds_next, ds_next_da) + + # (dr_ds' + dV_ds')*ds'_da + dr_da + dQ_da = dQ_ds_next_da + dr_da_reshaped + + # Now let's multiply -[(dr_ds' + dV_ds')*ds'_da + dr_da] by the actions a + # and then let's autodifferentiate w.r.t theta_A (actor NN's parameters) to finally get -dQ/dtheta_A + with tf.GradientTape() as tape: + tape.watch(actor_model.trainable_variables) + actions = self.eval(actor_model, state_batch) + + actions_reshaped = tf.reshape(actions,(batch_size,self.conf.nb_action,1)) + dQ_da_reshaped = tf.reshape(dQ_da,(batch_size,1,self.conf.nb_action)) + Q_neg = tf.matmul(-dQ_da_reshaped,actions_reshaped) + + # Also here we need a scalar so we compute the mean -Q across the batch + mean_Qneg = tf.math.reduce_mean(Q_neg) + + # Gradients of the actor loss w.r.t. actor's parameters + actor_grad = tape.gradient(mean_Qneg, actor_model.trainable_variables) + + return actor_grad \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_6/RL.py b/Results Double Integrator/Results set test/Code/N_try_6/RL.py new file mode 100644 index 0000000..fa60798 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/RL.py @@ -0,0 +1,233 @@ +import uuid +import math +import numpy as np +import tensorflow as tf + +class RL_AC: + def __init__(self, env, NN, conf, N_try): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :parma critic_type : (str) Activation function to use for the critic NN + :param LR_SCHEDULE : (bool) Flag to use a scheduler for the learning rates + :param boundaries_schedule_LR_C : (list) Boudaries of critic LR + :param values_schedule_LR_C : (list) Values of critic LR + :param boundaries_schedule_LR_A : (list) Boudaries of actor LR + :param values_schedule_LR_A : (list) Values of actor LR + :param CRITIC_LEARNING_RATE : (float) Learning rate for the critic network + :param ACTOR_LEARNING_RATE : (float) Learning rate for the policy network + :param fresh_factor : (float) Refresh factor + :param prioritized_replay_alpha : (float) α determines how much prioritization is used + :param prioritized_replay_eps : (float) It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero + :param UPDATE_LOOPS : (int array) Number of updates of both critic and actor performed every EP_UPDATE episodes + :param save_interval : (int) save NNs interval + :param env_RL : (bool) Flag RL environment + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param MC : (bool) Flag to use MC or TD(n) + :param nsteps_TD_N : (int) Number of lookahed steps if TD(n) is used + :param UPDATE_RATE : (float) Homotopy rate to update the target critic network if TD(n) is used + :param cost_weights_terminal : (float array) Running cost weights vector + :param cost_weights_running : (float array) Terminal cost weights vector + :param dt : (float) Timestep + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param NNs_path : (str) NNs path + :param NSTEPS : (int) Max episode length + + ''' + self.env = env + self.NN = NN + self.conf = conf + + self.N_try = N_try + + self.actor_model = None + self.critic_model = None + self.target_critic = None + self.actor_optimizer = None + self.critic_optimizer = None + + self.init_rand_state = None + self.NSTEPS_SH = 0 + self.control_arr = None + self.state_arr = None + self.ee_pos_arr = None + self.exp_counter = np.zeros(conf.REPLAY_SIZE) + + return + + def setup_model(self, recover_training=None): + ''' Setup RL model ''' + # Create actor, critic and target NNs + self.actor_model = self.NN.create_actor() + + if self.conf.critic_type == 'elu': + self.critic_model = self.NN.create_critic_elu() + self.target_critic = self.NN.create_critic_elu() + elif self.conf.critic_type == 'sine': + self.critic_model = self.NN.create_critic_sine() + self.target_critic = self.NN.create_critic_sine() + elif self.conf.critic_type == 'sine-elu': + self.critic_model = self.NN.create_critic_sine_elu() + self.target_critic = self.NN.create_critic_sine_elu() + else: + self.critic_model = self.NN.create_critic_relu() + self.target_critic = self.NN.create_critic_relu() + + # Set optimizer specifying the learning rates + if self.conf.LR_SCHEDULE: + # Piecewise constant decay schedule + self.CRITIC_LR_SCHEDULE = tf.keras.optimizers.schedules.PiecewiseConstantDecay(self.conf.boundaries_schedule_LR_C, self.conf.values_schedule_LR_C) + self.ACTOR_LR_SCHEDULE = tf.keras.optimizers.schedules.PiecewiseConstantDecay(self.conf.boundaries_schedule_LR_A, self.conf.values_schedule_LR_A) + self.critic_optimizer = tf.keras.optimizers.Adam(self.CRITIC_LR_SCHEDULE) + self.actor_optimizer = tf.keras.optimizers.Adam(self.ACTOR_LR_SCHEDULE) + else: + self.critic_optimizer = tf.keras.optimizers.Adam(self.conf.CRITIC_LEARNING_RATE) + self.actor_optimizer = tf.keras.optimizers.Adam(self.conf.ACTOR_LEARNING_RATE) + + # Set initial weights of the NNs + if recover_training is not None: + NNs_path_rec = str(recover_training[0]) + N_try = recover_training[1] + update_step_counter = recover_training[2] + self.actor_model.load_weights("{}/N_try_{}/actor_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + self.critic_model.load_weights("{}/N_try_{}/critic_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + self.target_critic.load_weights("{}/N_try_{}/target_critic_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + else: + self.target_critic.set_weights(self.critic_model.get_weights()) + + def update(self, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, term_batch, weights_batch, batch_size=None): + ''' Update both critic and actor ''' + # Update the critic backpropagating the gradients + critic_grad, reward_to_go_batch, critic_value, target_critic_value = self.NN.compute_critic_grad(self.critic_model, self.target_critic, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, weights_batch) + self.critic_optimizer.apply_gradients(zip(critic_grad, self.critic_model.trainable_variables)) + + # Update the actor backpropagating the gradients + actor_grad = self.NN.compute_actor_grad(self.actor_model, self.critic_model, state_batch, term_batch, batch_size) + self.actor_optimizer.apply_gradients(zip(actor_grad, self.actor_model.trainable_variables)) + + return reward_to_go_batch, critic_value, target_critic_value + + @tf.function + def update_target(self, target_weights, weights): + ''' Update target critic NN ''' + tau = self.conf.UPDATE_RATE + for (a, b) in zip(target_weights, weights): + a.assign(b * tau + a * (1 - tau)) + + def learn_and_update(self, update_step_counter, buffer, ep): + ''' Sample experience and update buffer priorities and NNs ''' + for i in range(int(self.conf.UPDATE_LOOPS[ep])): + # Sample batch of transitions from the buffer + state_batch, partial_reward_to_go_batch, state_next_rollout_batch, dVdx_batch, d_batch, term_batch, weights_batch, batch_idxes = buffer.sample() + + # Update both critic and actor + reward_to_go_batch, critic_value, target_critic_value = self.update(state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, term_batch, weights_batch) + + # Update buffer priorities + if self.conf.prioritized_replay_alpha != 0: + buffer.update_priorities(batch_idxes, reward_to_go_batch, critic_value, target_critic_value) + + # Update target critic + if not self.conf.MC: + self.update_target(self.target_critic.variables, self.critic_model.variables) + + update_step_counter += 1 + + # Plot rollouts and save the NNs every conf.log_rollout_interval-training episodes + if update_step_counter%self.conf.save_interval == 0: + self.RL_save_weights(update_step_counter) + + return update_step_counter + + def RL_Solve(self, TO_controls, TO_states, TO_step_cost): + ''' Solve RL problem ''' + ep_return = 0 # Initialize the return + rwrd_arr = np.empty(self.NSTEPS_SH+1) # Reward array + state_next_rollout_arr = np.zeros((self.NSTEPS_SH+1, self.conf.nb_state)) # Next state array + partial_reward_to_go_arr = np.empty(self.NSTEPS_SH+1) # Partial cost-to-go array + total_reward_to_go_arr = np.empty(self.NSTEPS_SH+1) # Total cost-to-go array + term_arr = np.zeros(self.NSTEPS_SH+1) # Episode-termination flag array + term_arr[-1] = 1 + done_arr = np.zeros(self.NSTEPS_SH+1) # Episode-MC-termination flag array + + # START RL EPISODE + self.control_arr = TO_controls # action clipped in TO + + if self.conf.env_RL: + for step_counter in range(self.NSTEPS_SH): + # Simulate actions and retrieve next state and compute reward + self.state_arr[step_counter+1,:], rwrd_arr[step_counter] = self.env.step(self.conf.cost_weights_running, self.state_arr[step_counter,:], self.control_arr[step_counter,:]) + + # Compute end-effector position + self.ee_pos_arr[step_counter+1,:] = self.env.get_end_effector_position(self.state_arr[step_counter+1, :]) + rwrd_arr[-1] = self.env.reward(self.conf.cost_weights_terminal, self.state_arr[-1,:]) + else: + self.state_arr, rwrd_arr = TO_states, -TO_step_cost + + ep_return = sum(rwrd_arr) + + # Store transition after computing the (partial) cost-to go when using n-step TD (from 0 to Monte Carlo) + for i in range(self.NSTEPS_SH+1): + # set final lookahead step depending on whether Monte Cartlo or TD(n) is used + if self.conf.MC: + final_lookahead_step = self.NSTEPS_SH + done_arr[i] = 1 + else: + final_lookahead_step = min(i+self.conf.nsteps_TD_N, self.NSTEPS_SH) + if final_lookahead_step == self.NSTEPS_SH: + done_arr[i] = 1 + else: + state_next_rollout_arr[i,:] = self.state_arr[final_lookahead_step+1,:] + + # Compute the partial and total cost to go + partial_reward_to_go_arr[i] = np.float32(sum(rwrd_arr[i:final_lookahead_step+1])) + total_reward_to_go_arr[i] = np.float32(sum(rwrd_arr[i:self.NSTEPS_SH+1])) + + return self.state_arr, partial_reward_to_go_arr, total_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, self.ee_pos_arr + + def RL_save_weights(self, update_step_counter='final'): + ''' Save NN weights ''' + self.actor_model.save_weights(self.conf.NNs_path+"/N_try_{}/actor_{}.h5".format(self.N_try,update_step_counter)) + self.critic_model.save_weights(self.conf.NNs_path+"/N_try_{}/critic_{}.h5".format(self.N_try,update_step_counter)) + self.target_critic.save_weights(self.conf.NNs_path+"/N_try_{}/target_critic_{}.h5".format(self.N_try,update_step_counter)) + + def create_TO_init(self, ep, ICS): + ''' Create initial state and initial controls for TO ''' + self.init_rand_state = ICS + + self.NSTEPS_SH = self.conf.NSTEPS - int(self.init_rand_state[-1]/self.conf.dt) + if self.NSTEPS_SH == 0: + return None, None, None, None, 0 + + # Initialize array to store RL state, control, and end-effector trajectories + self.control_arr = np.empty((self.NSTEPS_SH, self.conf.nb_action)) + self.state_arr = np.empty((self.NSTEPS_SH+1, self.conf.nb_state)) + self.ee_pos_arr = np.empty((self.NSTEPS_SH+1,3)) + + # Set initial state and end-effector position + self.state_arr[0,:] = self.init_rand_state + self.ee_pos_arr[0,:] = self.env.get_end_effector_position(self.state_arr[0, :]) + + # Initialize array to initialize TO state and control variables + init_TO_controls = np.zeros((self.NSTEPS_SH, self.conf.nb_action)) + init_TO_states = np.zeros(( self.NSTEPS_SH+1, self.conf.nb_state)) + + # Set initial state + init_TO_states[0,:] = self.init_rand_state + + # Simulate actor's actions to compute the state trajectory used to initialize TO state variables (use ICS for state and 0 for control if it is the first episode otherwise use policy rollout) + success_init_flag = 1 + for i in range(self.NSTEPS_SH): + if ep == 0: + init_TO_controls[i,:] = np.zeros(self.conf.nb_action) + else: + init_TO_controls[i,:] = tf.squeeze(self.NN.eval(self.actor_model, np.array([init_TO_states[i,:]]))).numpy() + init_TO_states[i+1,:] = self.env.simulate(init_TO_states[i,:],init_TO_controls[i,:]) + if np.isnan(init_TO_states[i+1,:]).any(): + success_init_flag = 0 + return None, None, None, None, success_init_flag + + return self.init_rand_state, init_TO_states, init_TO_controls, self.NSTEPS_SH, success_init_flag \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_6/TO.py b/Results Double Integrator/Results set test/Code/N_try_6/TO.py new file mode 100644 index 0000000..2468938 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/TO.py @@ -0,0 +1,202 @@ +import sys +import math +import casadi +import numpy as np +import pinocchio.casadi as cpin + +class TO_Casadi: + + def __init__(self, env, conf, env_TO, w_S=0): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param u_min : (float array) Action lower bound array + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param dt : (float) Timestep + + :input system_id : (str) Id system + + :input w_S : (float) Sobolev-training weight + ''' + + self.env = env + self.conf = conf + + self.nx = conf.nx + self.nu = conf.na + + self.w_S = w_S + + self.CAMS = env_TO + + def TO_System_Solve(self, ICS_state, init_TO_states, init_TO_controls, T): + ''' Create and solbe TO casadi problem ''' + ### PROBLEM + opti = casadi.Opti() + + # The control models are stored as a collection of shooting nodes called running models, with an additional terminal model. + self.runningSingleModel = self.CAMS('running_model', self.conf) + runningModels = [ self.runningSingleModel for _ in range(T) ] + self.terminalModel = self.CAMS('terminal_model', self.conf) + + # Decision variables + xs = [ opti.variable(model.nx) for model in runningModels+[self.terminalModel] ] # state variable + us = [ opti.variable(model.nu) for model in runningModels ] # control variable + + # Roll out loop, summing the integral cost and defining the shooting constraints. + total_cost = 0 + + opti.subject_to(xs[0] == ICS_state[:-1]) + + for t in range(T): + x_next, r_cost = runningModels[t].step_fun(xs[t], us[t]) + opti.subject_to(xs[t + 1] == x_next ) + total_cost += r_cost + r_cost_final = self.terminalModel.cost(xs[-1], us[-1]) + total_cost += r_cost_final + + ### SOLVE + opti.minimize(total_cost) + + # Create warmstart + init_x_TO = [np.array(init_TO_states[i,:-1]) for i in range(T+1)] + init_u_TO = [np.array(init_TO_controls[i,:]) for i in range(T)] + + for x,xg in zip(xs,init_x_TO): opti.set_initial(x,xg) + for u,ug in zip(us,init_u_TO): opti.set_initial(u,ug) + + # Set solver options + #opts = {'ipopt.linear_solver':'ma57', 'ipopt.sb': 'yes','ipopt.print_level': 0, 'print_time': 0} #, 'ipopt.max_iter': 500} + opts = {'ipopt.sb': 'yes','ipopt.print_level': 0, 'print_time': 0} #, 'ipopt.max_iter': 500} + opti.solver("ipopt", opts) + + try: + opti.solve() + TO_states = np.array([ opti.value(x) for x in xs ]) + TO_controls = np.array([ opti.value(u) for u in us ]) + TO_total_cost = opti.value(total_cost) + TO_ee_pos_arr = np.empty((T+1,3)) + TO_step_cost = np.empty(T+1) + for n in range(T): + TO_ee_pos_arr[n,:] = np.reshape(runningModels[n].p_ee(TO_states[n,:]),-1) + TO_step_cost[n] = runningModels[n].cost(TO_states[n,:], TO_controls[n,:]) + TO_ee_pos_arr[-1,:] = np.reshape(self.terminalModel.p_ee(TO_states[-1,:]),-1) + TO_step_cost[-1] = self.terminalModel.cost(TO_states[-1,:], TO_controls[-1,:]) + success_flag = 1 + except: + print('ERROR in convergence, returning debug values') + TO_states = np.array([ opti.debug.value(x) for x in xs ]) + TO_controls = np.array([ opti.debug.value(u) for u in us ]) + TO_total_cost = None + TO_ee_pos_arr = None + TO_step_cost = None + success_flag = 0 + + return success_flag, TO_controls, TO_states, TO_ee_pos_arr, TO_total_cost, TO_step_cost + + def TO_Solve(self, ICS_state, init_TO_states, init_TO_controls, T): + ''' Retrieve TO problem solution and compute the value function derviative with respect to the state ''' + success_flag, TO_controls, TO_states, TO_ee_pos_arr, _, TO_step_cost = self.TO_System_Solve(ICS_state, init_TO_states, init_TO_controls, T) + if success_flag == 0: + return None, None, success_flag, None, None, None + + if self.w_S != 0: + # Compute V gradient w.r.t. x (no computation dV/dt) + dVdx = self.backward_pass(T+1, TO_states, TO_controls) + else: + dVdx = np.zeros((T+1, self.conf.nb_state)) + + # Add the last state component (time) + TO_states = np.concatenate((TO_states, init_TO_states[0,-1] + np.transpose(self.conf.dt*np.array([range(T+1)]))), axis=1) + + return TO_controls, TO_states, success_flag, TO_ee_pos_arr, TO_step_cost, dVdx + + def backward_pass(self, T, TO_states, TO_controls, mu=1e-9): + ''' Perform the backward-pass of DDP to obtain the derivatives of the Value function w.r.t x ''' + n = self.conf.nb_state-1 + m = self.conf.nb_action + + X_bar = np.zeros((T, n)) + for i in range(n): + X_bar[:,i] = [TO_states[t,i] for t in range(T)] + + U_bar = np.zeros((T-1, m)) + for i in range(m): + U_bar[:,i] = [TO_controls[t,i] for t in range(T-1)] + + # The task is defined by a quadratic cost: + # sum_{i=0}^T 0.5 x' l_{xx,i} x + l_{x,i} x + 0.5 u' l_{uu,i} u + l_{u,i} u + x' l_{xu,i} u + l_x = np.zeros((T, n)) + l_xx = np.zeros((T, n, n)) + l_u = np.zeros((T-1, m)) + l_uu = np.zeros((T-1, m, m)) + l_xu = np.zeros((T-1, n, m)) + + # The cost-to-go is defined by a quadratic function: 0.5 x' Q_{xx,i} x + Q_{x,i} x + ... + Q_xx = np.zeros((T-1, n, n)) + Q_x = np.zeros((T-1, n)) + Q_uu = np.zeros((T-1, m, m)) + Q_u = np.zeros((T-1, m)) + Q_xu = np.zeros((T-1, n, m)) + + x = casadi.SX.sym('x',n,1) + u = casadi.SX.sym('u',m,1) + + running_cost = -self.runningSingleModel.cost(x, u) + terminal_cost = -self.terminalModel.cost(x, u) + + running_cost_xx, running_cost_x = casadi.hessian(running_cost,x) + running_cost_uu, running_cost_u = casadi.hessian(running_cost,u) + running_cost_xu = casadi.jacobian(casadi.jacobian(running_cost,x),u) + terminal_cost_xx, terminal_cost_x = casadi.hessian(terminal_cost,x) + + fun_running_cost_x = casadi.Function('fun_running_cost_x', [x], [running_cost_x], ['x'], ['running_cost_x']) + fun_running_cost_xx = casadi.Function('fun_running_cost_xx', [x], [running_cost_xx], ['x'], ['running_cost_xx']) + fun_running_cost_xu = casadi.Function('fun_running_cost_xu', [x,u],[running_cost_xu], ['x','u'], ['running_cost_xu']) + fun_running_cost_u = casadi.Function('fun_running_cost_u', [u], [running_cost_u], ['u'], ['running_cost_u']) + fun_running_cost_uu = casadi.Function('fun_running_cost_uu', [u], [running_cost_uu], ['u'], ['running_cost_uu']) + fun_terminal_cost_x = casadi.Function('fun_terminal_cost_x', [x], [terminal_cost_x], ['x'], ['terminal_cost_x']) + fun_terminal_cost_xx = casadi.Function('fun_terminal_cost_xx',[x], [terminal_cost_xx], ['x'], ['terminal_cost_xx']) + + # The Value function is defined by a quadratic function: 0.5 x' V_{xx,i} x + V_{x,i} x + V_xx = np.zeros((T, n, n)) + V_x = np.zeros((T, n+1)) + + # Dynamics derivatives w.r.t. x and u + A = np.zeros((T-1, n, n)) + B = np.zeros((T-1, n, m)) + + # Initialize value function + l_x[-1,:], l_xx[-1,:,:] = np.reshape(fun_terminal_cost_x(X_bar[-1,:]),n), fun_terminal_cost_xx(X_bar[-1,:]) + V_xx[T-1,:,:] = l_xx[-1,:,:] + V_x[T-1,:-1] = l_x[-1,:] + + for i in range(T-2, -1, -1): + # Compute dynamics Jacobians + A[i,:,:], B[i,:,:] = self.env.augmented_derivative(X_bar[i,:], U_bar[i,:]) + + # Compute the gradient of the cost function at X=X_bar + l_x[i,:], l_xx[i,:,:] = np.reshape(fun_running_cost_x(X_bar[i,:]),n), fun_running_cost_xx(X_bar[i,:]) + l_u[i,:],l_uu[i,:,:] = np.reshape(fun_running_cost_u(U_bar[i,:]),m), fun_running_cost_uu(U_bar[i,:]) + l_xu[i,:,:] = fun_running_cost_xu(X_bar[i,:], U_bar[i,:]) + + # Compute regularized cost-to-go + Q_x[i,:] = l_x[i,:] + A[i,:,:].T @ V_x[i+1,:-1] + Q_u[i,:] = l_u[i,:] + B[i,:,:].T @ V_x[i+1,:-1] + Q_xx[i,:,:] = l_xx[i,:,:] + A[i,:,:].T @ V_xx[i+1,:,:] @ A[i,:,:] + Q_uu[i,:,:] = l_uu[i,:,:] + B[i,:,:].T @ V_xx[i+1,:,:] @ B[i,:,:] + Q_xu[i,:,:] = l_xu[i,:,:] + A[i,:,:].T @ V_xx[i+1,:,:] @ B[i,:,:] + + Qbar_uu = Q_uu[i,:,:] + mu*np.identity(m) + Qbar_uu_pinv = np.linalg.pinv(Qbar_uu) + + # Compute the derivative of the Value function w.r.t. x + V_x[i,:-1] = Q_x[i,:] - Q_xu[i,:,:] @ Qbar_uu_pinv @ Q_u[i,:] + V_xx[i,:] = Q_xx[i,:] - Q_xu[i,:,:] @ Qbar_uu_pinv @ Q_xu[i,:,:].T + + return V_x \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_6/conf_car.py b/Results Double Integrator/Results set test/Code/N_try_6/conf_car.py new file mode 100644 index 0000000..c4d72bb --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/conf_car.py @@ -0,0 +1,203 @@ +import math +import numpy as np + +system_id = 'car' + +''' CACTO parameters ''' +EP_UPDATE = 250 # Number of episodes before updating critic and actor +NUPDATES = 260000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 38000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 500 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 10000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 1e2 # Distance from target weight +w_u = 1e1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Car/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Car/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Car/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Car/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Car/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Car/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Car/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' # Link mass + +### State parameters +nb_state = 5 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 5 +na = 2 +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, -math.pi, -10, -3, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, math.pi, 10, 3, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, math.pi, 10, 3, int(NSTEPS*dt)]) # Array used to normalize states +# state: x, y, theta, v, a, t + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-10.0, 10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-10.0, -10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([12.0, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([12.0, -2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([15.0, 0.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +bound_actions = 0 +omega_lower_bound = -2 # Action lower bound +omega_upper_bound = 2 +jerk_lower_bound = -1 +jerk_upper_bound = 1 # Action upper bound +u_min = np.array([omega_lower_bound, jerk_lower_bound]) # Action lower bound vector +u_max = np.array([omega_upper_bound, jerk_upper_bound]) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_6/conf_car_park.py b/Results Double Integrator/Results set test/Code/N_try_6/conf_car_park.py new file mode 100644 index 0000000..05c66c1 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/conf_car_park.py @@ -0,0 +1,221 @@ +import math +import numpy as np + +system_id = 'car_park' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 260000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 38000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/2) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 10000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 0 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -10 # X coord center ellipse 1 +YC1 = 6.75 # Y coord center ellipse 1 +A1 = 17 # Width ellipse 1 +B1 = 4.5 # Height ellipse 1 +XC2 = 10 # X coord center ellipse 2 +YC2 = 6.75 # Y coord center ellipse 2 +A2 = 17 # Width ellipse 2 +B2 = 4.5 # Height ellipse 2 +XC3 = 0 # X coord center ellipse 2 +YC3 = -2 # Y coord center ellipse 2 +A3 = 40 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +L = 4.35 +W = 2 +L_delta = 2.63 +tau_delta = 1 + +check_points_BF = np.array([[-L/2, W/2], + [-L/2+L/3, W/2], + [-L/2+2/3*L, W/2], + [L/2, W/2], + [L/2, 0], + [L/2, -W/2], + [-L/2+2/3*L, -W/2], + [-L/2+L/3, -W/2], + [-L/2, -W/2], + [-L/2, 0]]) # Check points in BF # Check points in BF + +### Weigths +w_d = 1e2 # Distance from target weight +w_u = 1e1 # Control effort weight +w_peak = 1e6 # Target threshold weight +w_ob = 5e4 # Obstacle weight +w_v = 1e2 # Velocity weight +delta_bound = 2*np.pi/6 +w_delta_bound = 0 # Delta bound weight (not implemented) +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u, w_delta_bound]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, w_v, w_ob, w_ob, w_ob, 0, w_delta_bound]) # Terminal cost weights vector + +k_db = 50 + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 1 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = 0 # Target x position +y_des = 6.75 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Car Park/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Car Park/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Car Park/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Car Park/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Car Park/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Car Park/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Car Park/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' # Link mass + +### State parameters +nb_state = 5 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 5 +na = 2 + +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-10, 1.5, -math.pi/6, 0, 0, 0]) #1.5 #-2.5 # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 10, 3, math.pi/6, 0, 0, (NSTEPS-1)*dt]) #3.0 #10 # State upper bound initial configuration array +state_norm_arr = np.array([10, 3, math.pi, 10, math.pi/6, int(NSTEPS*dt)]) # Array used to normalize states +# state: x, y, theta, v, delta, t + +# initial configurations for plot.rollout() +init_states_sim = [np.array([-9.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-5.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-2.5 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([0.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([2.5 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([5.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([9.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +acc_lower_bound = -3 # Action lower bound +acc_upper_bound = 3 +delta_dot_lower_bound = -1 +delta_dot_upper_bound = 1 # Action upper bound +u_min = np.array([acc_lower_bound, delta_dot_lower_bound]) # Action lower bound vector +u_max = np.array([acc_upper_bound, delta_dot_upper_bound]) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-11, 11], [-2.5, 10]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_6/conf_double_integrator.py b/Results Double Integrator/Results set test/Code/N_try_6/conf_double_integrator.py new file mode 100644 index 0000000..03b56ca --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/conf_double_integrator.py @@ -0,0 +1,213 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'double_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 50000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 18000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 200 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-4 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 1 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Double Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Double Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Double Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Double Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Double Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Double Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Double Integrator/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Robot upload data +URDF_FILENAME = "double_integrator.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + +q_init, v_init = np.array([-5, 0]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max) + +### State parameters +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, dt]) # State lower bound vector +x_init_min = np.array([-15, -15, -6, -6, dt]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, 6, 6, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, 6, 6, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([12.0, 2.0, 0.0, 0.0, 0.0]), + np.array([12.0, -2.0, 0.0, 0.0, 0.0]), + np.array([15.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = robot.na # Action size +tau_lower_bound = -2 # Action lower bound +tau_upper_bound = 2 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-15, 15], [-15, 15]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_6/conf_manipulator.py b/Results Double Integrator/Results set test/Code/N_try_6/conf_manipulator.py new file mode 100644 index 0000000..6b5d539 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/conf_manipulator.py @@ -0,0 +1,225 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'manipulator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 380000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 50000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network + nsteps_TD_N = int(NSTEPS/2) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 15000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 0 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 24000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' + +NH1 = 256 # 1st hidden layer size +NH2 = 256 # 2nd hidden layer size + +LR_SCHEDULE = 1 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 1e4 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, w_v, w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 50 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -20.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Manipulator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Manipulator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Manipulator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Manipulator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Manipulator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Manipulator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Manipulator/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Robot upload data +URDF_FILENAME = "planar_manipulator_3dof.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + +q_init, v_init = np.array([math.pi, math.pi, math.pi]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max) + +### System configuration parameters +x_base = -7.0 # x coord base +y_base = 0.0 # y coord base + +### State parameters +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-math.pi, -math.pi, -math.pi, -math.pi/4, -math.pi/4, -math.pi/4, 0]) # State lower bound initial configuration vector +x_max = np.array([ np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ math.pi, math.pi, math.pi, math.pi/4, math.pi/4, math.pi/4, (NSTEPS-1)*dt]) # State upper bound initial configuration vector +state_norm_arr = np.array([15,15,15,10,10,10,int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([math.pi/4, -math.pi/8, -math.pi/8, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, math.pi/8, math.pi/8, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + #np.array([ 1.34565955, -2.39833441, 0.87800266, 0., 0., 0., 0. ]), + #np.array([-1.34565955, 2.39833441, -0.87800266, 0., 0., 0., 0. ]), + np.array([-1.55135003, 2.93707696, -1.3025857 , 0., 0., 0., 0. ]), + np.array([ 1.55135003, -2.93707696, 1.3025857 , 0., 0., 0., 0. ]), + np.array([-1.31811607, 2.63623214, -1.31811607, 0., 0., 0., 0. ]), + np.array([-0.98843209, 1.97686418, -0.98843209, 0., 0., 0., 0. ])] + #np.array([-1.05348883, 1.9057266 , -0.61849459, 0., 0., 0., 0. ])] + #np.array([ 1.05348883, -1.9057266 , 0.61849459, 0., 0., 0., 0. ])] + +### Action parameters +nb_action = robot.na # Action size +tau_lower_bound = -200 # Action lower bound +tau_upper_bound = 200 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-41, 31], [-35, 35]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_6/conf_single_integrator.py b/Results Double Integrator/Results set test/Code/N_try_6/conf_single_integrator.py new file mode 100644 index 0000000..02f767d --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/conf_single_integrator.py @@ -0,0 +1,200 @@ +import numpy as np + +system_id = 'single_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 100000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 25000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Single Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Single Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Single Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Single Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Single Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Single Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = 'set prova b' +NNs_path_rec = './Results Single Integrator/Results {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = 0 +update_step_counter_rec = 12000 + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + + + +### State parameters +nb_state = 2 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 2 +na = 2 +x_min = np.array([-np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0]), + np.array([10.0, 10.0, 0.0]), + np.array([-10.0, 10.0, 0.0]), + np.array([-10.0, -10.0, 0.0]), + np.array([12.0, 2.0, 0.0]), + np.array([12.0, -2.0, 0.0]), + np.array([15.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +tau_lower_bound = -6 # Action lower bound +tau_upper_bound = 6 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_6/conf_ur5.py b/Results Double Integrator/Results set test/Code/N_try_6/conf_ur5.py new file mode 100644 index 0000000..1521ac0 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/conf_ur5.py @@ -0,0 +1,227 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'ur5' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 380000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 50000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + # 2nd hidden layer size + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +# Obstacles parameters +XC1 = 0.0 # X coord center ellipse 1 +YC1 = 0.25 # Y coord center ellipse 1 +ZC1 = 0.2 +ell1_center = [XC1, YC1, ZC1] +A1 = 0.5 # Width ellipse 1 +B1 = 0.2 # Height ellipse 1 +C1 = 0.34 +XC2 = 0.2 # X coord center ellipse 2 +YC2 = 0.425 # Y coord center ellipse 2 +ZC2 = 0.2 +ell2_center = [XC2, YC2, ZC2] +A2 = 0.4 # Width ellipse 2 +B2 = 0.14 # Height ellipse 2 +C2 = 0.34 +XC3 = -0.2 # X coord center ellipse 2 +YC3 = 0.425 # Y coord center ellipse 2 +ZC3 = 0.2 +ell3_center = [XC3, YC3, ZC3] +A3 = 0.4 # Width ellipse 2 +B3 = 0.14 # Height ellipse 2 +C3 = 0.34 +obs_param = np.array([XC1, YC1, ZC1, XC2, YC2, ZC2, XC3, YC3, ZC3, A1, B1, C1, A2, B2, C2, A3, B3, C3]) # Obstacle parameters vector + +# Weigths +w_d = 100 # Distance from target weight +w_u = 1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +# SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +# Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +# Target parameters +x_des = 0.0 # Target x position +y_des = 0.425 # Target y position +z_des = 0.2 +TARGET_STATE = np.array([x_des, y_des, z_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results UR5/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results UR5/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results UR5/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results UR5/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results UR5/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results UR5/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results UR5/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + + + +''' System parameters ''' +# Robot upload data +URDF_FILENAME = "ur5_robot.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +# Dynamics parameters' +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' +use_viewer = False +simulate_real_time = True +show_floor = False +PRINT_T = 1 # print every PRINT_N time steps +DISPLAY_T = 0.02 # update robot configuration in viewer every DISPLAY_N time steps +# CAMERA_TRANSFORM = [3.6914889812469482, 0.4583563506603241, -0.05435386672616005, 0.48037904500961304, 0.5339481830596924, 0.5137122273445129, 0.4692920446395874] +CAMERA_TRANSFORM = [0.36461642384529114, 0.8866147994995117, 2.579286575317383, 0.03584412857890129, 0.14833784103393555, 0.9481053948402405, 0.27893954515457153] + +q_init, v_init = np.array([0.,-math.pi/2,0.,0.,0.,0.]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max, use_viewer, DISPLAY_T, CAMERA_TRANSFORM, show_floor) + +# State parameters +dt = 0.01 # Timestep +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-math.pi, -math.pi, -math.pi, -math.pi, -math.pi, -math.pi, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, 0]) # State lower bound initial configuration vector +x_max = np.array([ np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ math.pi, math.pi, math.pi, math.pi, math.pi, math.pi, math.pi/4, math.pi/4, math.pi/4, math.pi/4, math.pi/4, math.pi/4, (NSTEPS-1)*dt]) # State upper bound initial configuration vector +state_norm_arr = np.array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([math.pi/4, -math.pi/8, -math.pi/8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, math.pi/8, math.pi/8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])] + +# Action parameters +nb_action = robot.na # Action size +u_min = np.array([-150, -150, -150, -28, -28, -28]) # Action lower bound vector +u_max = np.array([150, 150, 150, 28, 28, 28]) # Action upper bound vector +w_b = 1/w_u + + +fig_ax_lim = np.array([[-3, 3], [-3, 3]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + +profile = 0 + +env_RL = 0 diff --git a/Results Double Integrator/Results set test/Code/N_try_6/environment.py b/Results Double Integrator/Results set test/Code/N_try_6/environment.py new file mode 100644 index 0000000..87bd97f --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/environment.py @@ -0,0 +1,816 @@ +import math +import mpmath +import random +import numpy as np +import tensorflow as tf +import pinocchio as pin + +from utils import * + +class Env: + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param simu : (RobotSimulator instance) + :param x_init_min : (float array) State lower bound initial configuration array + :param x_init_max : (float array) State upper bound initial configuration array + :param x_min : (float array) State lower bound vector + :param x_max : (float array) State upper bound vector + :param u_min : (float array) Action lower bound array + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + ''' + + self.conf = conf + + self.nq = conf.nq + self.nv = conf.nv + self.nx = conf.nx + self.nu = conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + def reset(self): + ''' Choose initial state uniformly at random ''' + state = np.zeros(self.conf.nb_state) + + time = random.uniform(self.conf.x_init_min[-1], self.conf.x_init_max[-1]) + for i in range(self.conf.nb_state-1): + state[i] = random.uniform(self.conf.x_init_min[i], self.conf.x_init_max[i]) + state[-1] = self.conf.dt*round(time/self.conf.dt) + + return state + + def check_ICS_feasible(self, state): + ''' Check if ICS is not feasible ''' + # check if ee is in the obstacles + p_ee = self.get_end_effector_position(state) + + ellipse1 = ((p_ee[0] - self.conf.XC1)**2) / ((self.conf.A1 / 2)**2) + ((p_ee[1] - self.conf.YC1)**2) / ((self.conf.B1 / 2)**2) + ellipse2 = ((p_ee[0] - self.conf.XC2)**2) / ((self.conf.A2 / 2)**2) + ((p_ee[1] - self.conf.YC2)**2) / ((self.conf.B2 / 2)**2) + ellipse3 = ((p_ee[0] - self.conf.XC3)**2) / ((self.conf.A3 / 2)**2) + ((p_ee[1] - self.conf.YC3)**2) / ((self.conf.B3 / 2)**2) + + feasible_flag = ellipse1 > 1 and ellipse2 > 1 and ellipse3 > 1 + + return feasible_flag + + def step(self, weights, state, action): + ''' Return next state and reward ''' + # compute next state + state_next = self.simulate(state, action) + + # compute reward + reward = self.reward(weights, state, action) + + return (state_next, reward) + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + # Simulate control action + self.conf.simu.simulate(np.copy(state[:-1]), action, self.conf.dt, 1) ### Explicit Euler ### + + # Return next state + state_next[:self.nq], state_next[self.nq:self.nx] = np.copy(self.conf.simu.q), np.copy(self.conf.simu.v) + state_next[-1] = state[-1] + self.conf.dt + + return state_next + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Create robot model in Pinocchio with q_init as initial configuration + q_init = state[:self.nq] + v_init = state[self.nq:self.nx] + + # Dynamics gradient w.r.t control (1st order euler) + pin.computeABADerivatives(self.conf.robot.model, self.conf.robot.data, np.copy(q_init), np.copy(v_init), action) + + Fu = np.zeros((self.nx+1, self.nu)) + Fu[self.nv:-1, :] = self.conf.robot.data.Minv + Fu[:self.nx, :] *= self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + q = state[:self.nq] + v = state[self.nq:self.nx] + + # Compute Jacobians for continuous time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + pin.computeABADerivatives(self.conf.robot.model, self.conf.robot.data, q, v, action) + + Fx[:self.nv, :self.nv] = 0.0 + Fx[:self.nv, self.nv:self.nx] = np.identity(self.nv) + Fx[self.nv:self.nx, :self.nv] = self.conf.robot.data.ddq_dq + Fx[self.nv:self.nx, self.nv:self.nx] = self.conf.robot.data.ddq_dv + Fu[self.nv:self.nx, :] = self.conf.robot.data.Minv + + # Convert them to discrete time + Fx = np.identity(self.conf.nb_state-1) + self.conf.dt * Fx + Fu *= self.conf.dt + + return Fx, Fu + + def simulate_batch(self, state, action): + ''' Simulate dynamics using tensors and compute its gradient w.r.t control. Batch-wise computation ''' + state_next = np.array([self.simulate(s, a) for s, a in zip(state, action)]) + + return tf.convert_to_tensor(state_next, dtype=tf.float32) + + def derivative_batch(self, state, action): + ''' Simulate dynamics using tensors and compute its gradient w.r.t control. Batch-wise computation ''' + Fu = np.array([self.derivative(s, a) for s, a in zip(state, action)]) + + return tf.convert_to_tensor(Fu, dtype=tf.float32) + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + q = state[:self.nq] + + RF = self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id) + + H = self.conf.robot.framePlacement(q, RF, recompute) + + p = H.translation + + return p + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + +class SingleIntegrator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[0,0] = self.conf.dt + Fu[1,1] = self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0], + [0, 1]]) + + Fu[0,0] = self.conf.dt + Fu[1,1] = self.conf.dt + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*action[0] + state_next[1] = state[1] + self.conf.dt*action[1] + state_next[2] = state[2] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + + return p + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class DoubleIntegrator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = [self.get_end_effector_position(state)[i] for i in range(2)] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = np.math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class Car(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[2,0] = self.conf.dt + Fu[4,1] = self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0, -self.conf.dt*state[3]*math.sin(state[2]) -self.conf.dt**2*state[4]*math.sin(state[2])/2, self.conf.dt*math.cos(state[2]), self.conf.dt**2*math.cos(state[2])/2], + [0, 1, self.conf.dt*state[3]*math.cos(state[2]) +self.conf.dt**2*state[4]*math.cos(state[2])/2, self.conf.dt*math.sin(state[2]), self.conf.dt**2*math.sin(state[2])/2], + [0, 0, 1, 0, 0], + [0, 0, 0, 1, self.conf.dt], + [0, 0, 0, 0, 1]]) + + Fu[2,0] = self.conf.dt + Fu[4,1] = self.conf.dt + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*state[3]*tf.cos(state[2]) + self.conf.dt**2*state[4]*tf.cos(state[2])/2 + state_next[1] = state[1] + self.conf.dt*state[3]*tf.sin(state[2]) + self.conf.dt**2*state[4]*tf.sin(state[2])/2 + state_next[2] = state[2] + self.conf.dt*action[0] + state_next[3] = state[3] + self.conf.dt*state[4] + state_next[4] = state[4] + self.conf.dt*action[1] + state_next[5] = state[5] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + + return p + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class CarPark(Car): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def check_ICS_feasible(self, state): + ''' Check if ICS is not feasible ''' + # check if ee is in the obstacles + x_ee, y_ee = self.get_end_effector_position(state)[:2] + theta_ee = state[2] + + for i in range(len(self.conf.check_points_BF)): + check_points_WF_i = np.array([x_ee, y_ee]) + np.array([[math.cos(theta_ee), -math.sin(theta_ee)], [math.sin(theta_ee), math.cos(theta_ee)]]).dot(self.conf.check_points_BF[i,:]) + obs_1 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC1,self.YC1,self.A1,self.B1) + obs_2 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC2,self.YC2,self.A2,self.B2) + obs_3 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC3,self.YC3,self.A3,self.B3) + + feasible_flag = obs_1 < 0.5 and obs_2 < 0.5 and obs_3 < 0.5 + if feasible_flag == 0: + return feasible_flag + + return feasible_flag + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[3,0] = self.conf.dt + Fu[4,1] = self.conf.dt/self.conf.tau_delta + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0, -self.conf.dt*state[3]*math.sin(state[2]), self.conf.dt*math.cos(state[2]), 0], + [0, 1, self.conf.dt*state[3]*math.cos(state[2]), self.conf.dt*math.sin(state[2]), 0], + [0, 0, 1, self.conf.dt*math.tan(state[4])/self.conf.L_delta, self.conf.dt*state[3]*mpmath.sec(state[4])**2/self.conf.L_delta], + [0, 0, 0, 1, 0], + [0, 0, 0, 0, 1]]) + + Fu[3,0] = self.conf.dt + Fu[4,1] = self.conf.dt/self.conf.tau_delta + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*state[3]*math.cos(state[2]) + state_next[1] = state[1] + self.conf.dt*state[3]*math.sin(state[2]) + state_next[2] = state[2] + self.conf.dt*state[3]*math.tan(state[4])/self.conf.L_delta + state_next[3] = state[3] + self.conf.dt*action[0] + state_next[4] = state[4] + self.conf.dt*action[1]/self.conf.tau_delta + state_next[5] = state[5] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + np.array([[math.cos(state[2]), -math.sin(state[2])], [math.sin(state[2]), math.cos(state[2])]]).dot(np.array([self.conf.L_delta/2,0])) + + return p + + def obs_cost_fun(self,x,y,x_step,y_step,Wx,Wy,fv=1,k=50): + k = self.conf.k_db + + term1 = 4 + 4 * (y - y_step + Wy/2)**2 * k**2 + term2 = 4 + 4 * (y - y_step - Wy/2)**2 * k**2 + term3 = 4 + 4 * (x - x_step + Wx/2)**2 * k**2 + term4 = 4 + 4 * (x - x_step - Wx/2)**2 * k**2 + obs_cost = (term1)**(-1/2) * fv * (-np.sqrt(term2) / 2 + (y - y_step - Wy/2) * k) * (term3)**(-1/2) * (term2)**(-1/2) * (np.sqrt(term1) / 2 + (y - y_step + Wy/2) * k) * (term4)**(-1/2) * (np.sqrt(term3) / 2 + (x - x_step + Wx/2) * k) * (-np.sqrt(term4) / 2 + (x - x_step - Wx/2) * k) + + return obs_cost + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + theta_ee = state[2] + + obs_cost = 0 + check_points_WF = np.dot(np.array([[np.cos(theta_ee), -np.sin(theta_ee)], [np.sin(theta_ee), np.cos(theta_ee)]]), self.conf.check_points_BF.T).T + np.array([x_ee, y_ee]) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC1, self.YC1, self.A1, self.B1)) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC2, self.YC2, self.A2, self.B2)) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC3, self.YC3, self.A3, self.B3)) + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 + 0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 + 0.1) - math.sqrt(0.1) - 0.1 )) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*state[3]**2 - weights[3]*obs_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class Manipulator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = [self.get_end_effector_position(state)[i] for i in range(2)] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term penalizing the FINAL joint velocity + if weights[2] != 0: + vel_cost = state[self.nq:self.nx].dot(state[self.nq:self.nx]) + else: + vel_cost = 0 + + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*vel_cost - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) #- weights[2]*vel_cost + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class UR5(Env): + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.ZC1 = self.conf.obs_param[2] + self.XC2 = self.conf.obs_param[3] + self.YC2 = self.conf.obs_param[4] + self.ZC2 = self.conf.obs_param[5] + self.XC3 = self.conf.obs_param[6] + self.YC3 = self.conf.obs_param[7] + self.ZC3 = self.conf.obs_param[8] + + self.A1 = self.conf.obs_param[9] + self.B1 = self.conf.obs_param[10] + self.C1 = self.conf.obs_param[11] + self.A2 = self.conf.obs_param[12] + self.B2 = self.conf.obs_param[13] + self.C2 = self.conf.obs_param[14] + self.A3 = self.conf.obs_param[15] + self.B3 = self.conf.obs_param[16] + self.C3 = self.conf.obs_param[17] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee, z_ee = self.get_end_effector_position(state) + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) + ((z_ee-self.ZC1)**2)/((self.C1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) + ((z_ee-self.ZC2)**2)/((self.C2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) + ((z_ee-self.ZC3)**2)/((self.C3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((z_ee-self.TARGET_STATE[2])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + if action is not None: + u_cost = action.dot(action) + else: + u_cost = 0 + + # Term penalizing the FINAL joint velocity + vel_cost = state[self.nq:self.nx].dot(state[self.nq:self.nx]) + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + (z_ee-self.TARGET_STATE[2])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*vel_cost - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) diff --git a/Results Double Integrator/Results set test/Code/N_try_6/environment_TO.py b/Results Double Integrator/Results set test/Code/N_try_6/environment_TO.py new file mode 100644 index 0000000..0cf4b63 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/environment_TO.py @@ -0,0 +1,778 @@ +import sys +import math +import casadi +import numpy as np +import pinocchio.casadi as cpin + +class SingleIntegrator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + p_ee[-1] = 0 + + return p_ee + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*u[0] + x_next[1] = x[1] + self.conf.dt*u[1] + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class DoubleIntegrator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class Car_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + p_ee[-1] = 0 + + return p_ee + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*x[3]*casadi.cos(x[2]) + self.conf.dt**2*x[4]*casadi.cos(x[2])/2 + x_next[1] = x[1] + self.conf.dt*x[3]*casadi.sin(x[2]) + self.conf.dt**2*x[4]*casadi.sin(x[2])/2 + x_next[2] = x[2] + self.conf.dt*u[0] + x_next[3] = x[3] + self.conf.dt*x[4] + x_next[4] = x[4] + self.conf.dt*u[1] + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class CarPark_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + np.array([[np.cos(cx[2]), -np.sin(cx[2])], [np.sin(cx[2]), np.cos(cx[2])]]).dot(np.array([self.conf.L_delta/2,0])) + p_ee[-1] = 0 + + return p_ee + + def obs_cost_fun(self, x, y, x_clip, y_clip, Wx, Wy, fv=1, k=50): + k = self.conf.k_db + obs_cost = (4 + 4 * (y - y_clip + Wy / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * fv * (-np.sqrt(4 + 4 * (y - y_clip - Wy / 2) ** 2 * k ** 2) / 2 + (y - y_clip - Wy / 2) * k) * (4 + 4 * (x - x_clip + Wx / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (4 + 4 * (y - y_clip - Wy / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (np.sqrt(4 + 4 * (y - y_clip + Wy / 2) ** 2 * k ** 2) / 2 + (y - y_clip + Wy / 2) * k) * (4 + 4 * (x - x_clip - Wx / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (np.sqrt(4 + 4 * (x - x_clip + Wx / 2) ** 2 * k ** 2) / 2 + (x - x_clip + Wx / 2) * k) * (-np.sqrt(4 + 4 * (x - x_clip - Wx / 2) ** 2 * k ** 2) / 2 + (x - x_clip - Wx / 2) * k) + + return obs_cost + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def rotation_matrix(self, angle): + ''' Compute the 2x2 rotation matrix for a given angle ''' + cos_theta = casadi.cos(angle) + sin_theta = casadi.sin(angle) + + rotation_mat = casadi.vertcat(casadi.horzcat(cos_theta, -sin_theta), + casadi.horzcat(sin_theta, cos_theta)) + return rotation_mat + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee_tmp = self.p_ee(x) + p_ee = p_ee_tmp[:2] + theta_ee = x[2] + + ### Penalties representing the obstacle ### + obs_cost = 0 + check_points_WF = casadi.mtimes(self.rotation_matrix(theta_ee), self.conf.check_points_BF.T).T + casadi.repmat(p_ee.T, self.conf.check_points_BF.shape[0], 1) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC1, self.YC1, self.A1, self.B1)) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC2, self.YC2, self.A2, self.B2)) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC3, self.YC3, self.A3, self.B3)) + + ### Control effort term ### + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 2*0.1 - 2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*x[3]**2 + self.weights[3]*obs_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*x[3]*casadi.cos(x[2]) + x_next[1] = x[1] + self.conf.dt*x[3]*casadi.sin(x[2]) + x_next[2] = x[2] + self.conf.dt*x[3]*casadi.tan(x[4])/self.conf.L_delta + x_next[3] = x[3] + self.conf.dt*u[0] + x_next[4] = x[4] + self.conf.dt*u[1]/self.conf.tau_delta + + return x_next + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class Manipulator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class UR5_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.ZC1 = self.conf.obs_param[2] + self.XC2 = self.conf.obs_param[3] + self.YC2 = self.conf.obs_param[4] + self.ZC2 = self.conf.obs_param[5] + self.XC3 = self.conf.obs_param[6] + self.YC3 = self.conf.obs_param[7] + self.ZC3 = self.conf.obs_param[8] + + self.A1 = self.conf.obs_param[9] + self.B1 = self.conf.obs_param[10] + self.C1 = self.conf.obs_param[11] + self.A2 = self.conf.obs_param[12] + self.B2 = self.conf.obs_param[13] + self.C2 = self.conf.obs_param[14] + self.A3 = self.conf.obs_param[15] + self.B3 = self.conf.obs_param[16] + self.C3 = self.conf.obs_param[17] + + self.TARGET_STATE = self.conf.TARGET_STATE + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC1)**2)/((self.conf.A1/2)**2) + ((p_ee[1]-self.conf.YC1)**2)/((self.conf.B1/2)**2) + ((p_ee[2]-self.conf.ZC1)**2)/((self.conf.C1/2)**2) - 1.0)) + 1)/self.conf.alpha + ell2_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC2)**2)/((self.conf.A2/2)**2) + ((p_ee[1]-self.conf.YC2)**2)/((self.conf.B2/2)**2) + ((p_ee[2]-self.conf.ZC2)**2)/((self.conf.C2/2)**2) - 1.0)) + 1)/self.conf.alpha + ell3_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC3)**2)/((self.conf.A3/2)**2) + ((p_ee[1]-self.conf.YC3)**2)/((self.conf.B3/2)**2) + ((p_ee[2]-self.conf.ZC3)**2)/((self.conf.C3/2)**2) - 1.0)) + 1)/self.conf.alpha + + ### Control effort term ### + u_cost = 0 + for i in range(self.nu): + u_cost += u[i]**2 + self.conf.w_b*(u[i]/self.conf.u_max[i])**10 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.conf.alpha2*-(np.sqrt((p_ee[0]-self.conf.TARGET_STATE[0])**2 +0.1) - np.sqrt(0.1) - 0.1 + np.sqrt((p_ee[1]-self.conf.TARGET_STATE[1])**2 +0.1) - np.sqrt(0.1) - 0.1 + np.sqrt((p_ee[2]-self.conf.TARGET_STATE[2])**2 +0.1) - np.sqrt(0.1) - 0.1)) + 1)/self.conf.alpha2 + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.conf.TARGET_STATE[0])**2 + (p_ee[1]-self.conf.TARGET_STATE[1])**2 + (p_ee[2]-self.conf.TARGET_STATE[2])**2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_6/generate_tests_set_script.py b/Results Double Integrator/Results set test/Code/N_try_6/generate_tests_set_script.py new file mode 100644 index 0000000..4c5f781 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/generate_tests_set_script.py @@ -0,0 +1,26 @@ +### Create file to be executed ### +import os +import shutil + +system_id = 'car' +system_id_short = 'C' + +n_runs = 10 + +w_S = 1e-3 +info = '{}'.format(1) + +offset = 0 if w_S ==0 else 100 + +seed_list = [29556, 5280, 739, 92, 10, 7298, 14, 264, 22135, 342] + +shutil.copy('Template.sh', '{}{}.sh'.format(system_id_short,info)) +with open('{}{}.sh'.format(system_id_short,info), 'a') as f: + for i in range(1,1 + n_runs): + f.write("nohup python3 -u main.py --system-id='{}' --test-n={} --seed={} --w-S={} > out/{}{}.txt &\n".format(system_id,offset+i,seed_list[i-1], w_S, system_id_short,offset+i)) + f.write("\necho 'Running scripts in parallel'\n") + f.write("echo 'PID of this script is:' $$\n") + f.write("wait\n") + f.write("echo 'Script done running'\n") + +os.makedirs('out', exist_ok=True) \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_6/main.py b/Results Double Integrator/Results set test/Code/N_try_6/main.py new file mode 100644 index 0000000..8121e8c --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/main.py @@ -0,0 +1,254 @@ +import os +import sys +import time +import shutil +import random +import argparse +import importlib +import numpy as np +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # {'0' -> show all logs, '1' -> filter out info, '2' -> filter out warnings} +import tensorflow as tf +from multiprocessing import Pool +from RL import RL_AC +from TO import TO_Casadi +from plot_utils import PLOT +from NeuralNetwork import NN +from replay_buffer import PrioritizedReplayBuffer, ReplayBuffer + +def parse_args(): + ''' Parse the arguments for CACTO training ''' + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + + parser.add_argument('--test-n', type=int, default=0, + help="Test number") + + parser.add_argument('--seed', type=int, default=0, + help="random and tf.random seed") + + parser.add_argument('--system-id', type=str, default='single_integrator', + choices=["single_integrator", "double_integrator", "car", "car_park", "manipulator", "ur5"], + help="System-id (single_integrator, double_integrator, car, manipulator, ur5") + + parser.add_argument('--recover-training-flag', type=bool, default=False, + choices=["True", "False"], + help="Flag to recover training") + ### Not tested ### + #parser.add_argument('--GPU-flag', type=bool, default=False, + # choices=["True", "False"], + # help="Flag to use GPU") + + parser.add_argument('--nb-cpus', type=int, default=10, + help="Number of TO problems solved in parallel") + + parser.add_argument('--w-S', type=float, default=0, + help="Sobolev training - weight of the value related error") + + args = parser.parse_args() + dict_args = vars(args) + + return dict_args + + + + +if __name__ == '__main__': + + args = parse_args() + + ############### Input ############### + N_try = args['test_n'] + + if args['seed'] == None: + seed = random.randint(1,100000) + else: + seed = args['seed'] + tf.random.set_seed(seed) # Set tensorflow seed + random.seed(seed) # Set random seed + + system_id = args['system_id'] + + recover_training_flag = args['recover_training_flag'] + + ### Not tested ### + #GPU_flag = args['GPU_flag'] + #if GPU_flag: + # os.environ["CUDA_VISIBLE_DEVICES"]="-1" + #print(tf.config.experimental.list_physical_devices('GPU')) + + nb_cpus = args['nb_cpus'] + + w_S = args['w_S'] + ######################################################### + + + + # Import configuration file and environment file + system_map = { + 'single_integrator': ('conf_single_integrator', 'SingleIntegrator', 'SingleIntegrator_CAMS'), + 'double_integrator': ('conf_double_integrator', 'DoubleIntegrator', 'DoubleIntegrator_CAMS'), + 'car': ('conf_car', 'Car', 'Car_CAMS'), + 'car_park': ('conf_car_park', 'CarPark', 'CarPark_CAMS'), + 'manipulator': ('conf_manipulator', 'Manipulator', 'Manipulator_CAMS'), + 'ur5': ('conf_ur5', 'UR5', 'UR5_CAMS') + } + try: + conf_module, env_class, env_TO_class = system_map[system_id] + conf = importlib.import_module(conf_module) + Environment = getattr(importlib.import_module('environment'), env_class) + Environment_TO = getattr(importlib.import_module('environment_TO'), env_TO_class) + except KeyError: + print('System {} not found'.format(system_id)) + sys.exit() + + # Create folders to store the results and the trained NNs + for path in conf.path_list: + os.makedirs(path + '/N_try_{}'.format(N_try), exist_ok=True) + os.makedirs(conf.Config_path, exist_ok=True) + + # Save configuration + params = [p for p in conf.__dict__ if not p.startswith("__")] + with open(conf.Config_path + '/config{}.txt'.format(N_try), 'w') as f: + for p in params: + f.write('{} = {}\n'.format(p, conf.__dict__[p])) + f.write('Seed = {}\n'.format(seed)) + f.write('w_S = {}'.format(w_S)) + + shutil.copy('{}.py'.format(conf_module), conf.Config_path + '/' + conf_module + '_{}.py'.format(N_try)) + with open(conf.Config_path + '/' + conf_module + '_{}.py'.format(N_try), 'a') as f: + f.write('\n\n# {}'.format(args)) + + # Copy all file with .py extension from /mydir to /mydestdir + for file in os.listdir("./"): + if file.endswith(".py"): + shutil.copy(os.path.join("./", file), os.path.join(conf.Code_path + '/N_try_{}'.format(N_try), file)) + + # Create empty txt file in Log_path to store the test info + open(conf.Log_path + '/info.txt', 'a').close() + + + + ### Create instances of the used classes ### + env = Environment(conf) # Create environment instances + env_TO = Environment_TO + NN_inst = NN(env, conf, w_S) # Create NN instance + TrOp = TO_Casadi(env, conf, env_TO, w_S) # Create TO instance + RLAC = RL_AC(env, NN_inst, conf, N_try) # Create RL instance + buffer = ReplayBuffer(conf) if conf.prioritized_replay_alpha == 0 else PrioritizedReplayBuffer(conf) # Create an empty (prioritized) replay buffer + plot_fun = PLOT(N_try, env, NN_inst, conf) # Create PLOT instance + + # Set initial weights of the NNs, initialize the counter of the updates and setup NN models + if recover_training_flag: + recover_training = np.array([conf.NNs_path_rec, conf.N_try_rec, conf.update_step_counter_rec]) + update_step_counter = conf.update_step_counter_rec + + RLAC.setup_model(recover_training) + else: + update_step_counter = 0 + + RLAC.setup_model() + + # Save initial weights of the NNs + RLAC.RL_save_weights(update_step_counter) + + # Plot initial rollouts + plot_fun.plot_traj_from_ICS(np.array(conf.init_states_sim), TrOp, RLAC, update_step_counter=update_step_counter,steps=conf.NSTEPS, init=0) + + # Initialize arrays to store the reward history of each episode and the average reward history of last 100 episodes + ep_arr_idx = 0 + ep_reward_arr = np.zeros(conf.NEPISODES-ep_arr_idx)*np.nan + + def compute_sample(args): + ''' Create samples solving TO problems starting from given ICS ''' + ep = args[0] + ICS = args[1] + + # Create initial TO # + init_rand_state, init_TO_states, init_TO_controls, NSTEPS_SH, success_init_flag = RLAC.create_TO_init(ep, ICS) + if success_init_flag == 0: + return None + + # Solve TO problem # + TO_controls, TO_states, success_flag, TO_ee_pos_arr, TO_step_cost, dVdx = TrOp.TO_Solve(init_rand_state, init_TO_states, init_TO_controls, NSTEPS_SH) + if success_flag == 0: + return None + + # Collect experiences + state_arr, partial_reward_to_go_arr, total_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, RL_ee_pos_arr = RLAC.RL_Solve(TO_controls, TO_states, TO_step_cost) + + if conf.env_RL == 0: + RL_ee_pos_arr = TO_ee_pos_arr + + return NSTEPS_SH, TO_controls, TO_ee_pos_arr, dVdx, state_arr.tolist(), partial_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, RL_ee_pos_arr + + def create_unif_TO_init(n_UICS=1): + ''' Create n uniformely distributed ICS ''' + # Create ICS TO # + init_rand_state = env.reset() + + return init_rand_state + + + + ### START TRAINING ### + if conf.profile: + import cProfile, pstats + + profiler = cProfile.Profile() + profiler.enable() + + time_start = time.time() + + for ep in range(conf.NLOOPS): + # Generate and store conf.EP_UPDATE random-uniform ICS + with Pool(nb_cpus) as p: + init_rand_state = p.map(create_unif_TO_init, range(conf.EP_UPDATE)) + + # Generate samples + with Pool(nb_cpus) as p: + tmp = p.map(compute_sample, zip(ep*np.ones(conf.EP_UPDATE), init_rand_state)) + + # Remove unsuccessful TO problems and update EP_UPDATE + tmp = [x for x in tmp if x is not None] + NSTEPS_SH, TO_controls, ee_pos_arr_TO, dVdx, state_arr, partial_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, ee_pos_arr_RL = zip(*tmp) + + # Update the buffer + buffer.add(state_arr, partial_reward_to_go_arr, state_next_rollout_arr, dVdx, done_arr, term_arr) + + # Update NNs + update_step_counter = RLAC.learn_and_update(update_step_counter, buffer, ep) + + # plot Critic value function + #plot_fun.plot_Critic_Value_function(RLAC.critic_model, update_step_counter, system_id) ### + + # Plot rollouts and state and control trajectories + if update_step_counter%conf.plot_rollout_interval_diff_loc == 0 or system_id == 'single_integrator' or system_id == 'double_integrator' or system_id == 'car_park' or system_id == 'car' or system_id == 'manipulator': + print("System: {} - N_try = {}".format(conf.system_id, N_try)) + plot_fun.plot_Critic_Value_function(RLAC.critic_model, update_step_counter, system_id) + plot_fun.plot_traj_from_ICS(np.array(conf.init_states_sim), TrOp, RLAC, update_step_counter=update_step_counter, ep=ep,steps=conf.NSTEPS, init=1) + + # Update arrays to store the reward history and its average + ep_reward_arr[ep_arr_idx:ep_arr_idx+len(tmp)] = ep_return + ep_arr_idx += len(tmp) + + for i in range(len(tmp)): + print("Episode {} ---> Return = {}".format(ep*len(tmp) + i, ep_return[i])) + + if update_step_counter > conf.NUPDATES: + break + + time_end = time.time() + print('Elapsed time: ', time_end-time_start) + + if conf.profile: + profiler.disable() + stats = pstats.Stats(profiler).sort_stats('cumtime') + stats.print_stats() + + # Plot returns + plot_fun.plot_Return(ep_reward_arr) + + # Save networks at the end of the training + RLAC.RL_save_weights() + + # Simulate the final policy + plot_fun.rollout(update_step_counter, RLAC.actor_model, conf.init_states_sim) diff --git a/Results Double Integrator/Results set test/Code/N_try_6/plot_utils.py b/Results Double Integrator/Results set test/Code/N_try_6/plot_utils.py new file mode 100644 index 0000000..55daab0 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/plot_utils.py @@ -0,0 +1,714 @@ +import math +import numpy as np +import tensorflow as tf +import matplotlib.pyplot as plt +from matplotlib import cm, colors +from matplotlib.patches import Ellipse, FancyBboxPatch, Rectangle +from matplotlib.transforms import Affine2D +import mpl_toolkits.mplot3d.art3d as art3d + +class PLOT(): + def __init__(self, N_try, env, NN, conf): + ''' + :input N_try : (Test number) + + :input env : (Environment instance) + + :input conf : (Configuration file) + :param fig_ax_lim : (float array) Figure axis limit [x_min, x_max, y_min, y_max] + :param Fig_path : (str) Figure path + :param NSTEPS : (int) Max episode length + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param NORMALIZE_INPUTS : (bool) Flag to normalize inputs (state) + :param state_norm_array : (float array) Array used to normalize states + :param dt : (float) Timestep + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + ''' + self.env = env + self.NN = NN + self.conf = conf + + self.N_try = N_try + + self.xlim = conf.fig_ax_lim[0].tolist() + self.ylim = conf.fig_ax_lim[1].tolist() + + # Set the ticklabel font size globally + plt.rcParams['xtick.labelsize'] = 22 + plt.rcParams['ytick.labelsize'] = 22 + plt.rcParams.update({'font.size': 20}) + + return + + def plot_obstaces(self, a=1): + if self.conf.system_id == 'car_park': + obs1 = Rectangle((self.conf.XC1-self.conf.A1/2, self.conf.YC1-self.conf.B1/2), self.conf.A1, self.conf.B1, 0.0,alpha=a) + obs1.set_facecolor([30/255, 130/255, 76/255, 1]) + obs2 = Rectangle((self.conf.XC2-self.conf.A2/2, self.conf.YC2-self.conf.B2/2), self.conf.A2, self.conf.B2, 0.0,alpha=a) + obs2.set_facecolor([30/255, 130/255, 76/255, 1]) + obs3 = Rectangle((self.conf.XC3-self.conf.A3/2, self.conf.YC3-self.conf.B3/2), self.conf.A3, self.conf.B3, 0.0,alpha=a) + obs3.set_facecolor([30/255, 130/255, 76/255, 1]) + + #rec1 = FancyBboxPatch((self.conf.XC1-self.conf.A1/2, self.conf.YC1-self.conf.B1/2), self.conf.A1, self.conf.B1,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec1.set_facecolor([30/255, 130/255, 76/255, 1]) + #rec2 = FancyBboxPatch((self.conf.XC2-self.conf.A2/2, self.conf.YC2-self.conf.B2/2), self.conf.A2, self.conf.B2,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec2.set_facecolor([30/255, 130/255, 76/255, 1]) + #rec3 = FancyBboxPatch((self.conf.XC3-self.conf.A3/2, self.conf.YC3-self.conf.B3/2), self.conf.A3, self.conf.B3,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec3.set_facecolor([30/255, 130/255, 76/255, 1]) + else: + obs1 = Ellipse((self.conf.XC1, self.conf.YC1), self.conf.A1, self.conf.B1, 0.0,alpha=a) + obs1.set_facecolor([30/255, 130/255, 76/255, 1]) + obs2 = Ellipse((self.conf.XC2, self.conf.YC2), self.conf.A2, self.conf.B2, 0.0,alpha=a) + obs2.set_facecolor([30/255, 130/255, 76/255, 1]) + obs3 = Ellipse((self.conf.XC3, self.conf.YC3), self.conf.A3, self.conf.B3, 0.0,alpha=a) + obs3.set_facecolor([30/255, 130/255, 76/255, 1]) + + return [obs1, obs2, obs3] + + def plot_Reward(self, plot_obs=0): + x = np.arange(-15, 15, 0.1) + y = np.arange(-10, 10, 0.1) + theta = np.pi/2 + ICS = np.array([np.array([i,j,0]) for i in x for j in y]) + state = np.array([self.compute_ICS(np.array([i,j,0]), 'car')[0] for i in x for j in y]) # for k in theta] + state[:,2] = theta + r = [self.env.reward(self.conf.cost_weights_running, s) for s in state] + mi = min(r) + ma = max(r) + norm = colors.Normalize(vmin=mi,vmax=ma) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + pti = ax.scatter(ICS[:,0], ICS[:,1], norm=norm, c=r, cmap=cm.get_cmap('hot_r')) + plt.colorbar(pti) + + if plot_obs: + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + + # Center and check points of 'car_park' system + #check_points_WF_i = np.array([[math.cos(theta), -math.sin(theta)], [math.sin(theta), math.cos(theta)]]).dot(self.conf.check_points_BF[0,:]) + ICS[0,:2] + #ax.scatter(check_points_WF_i[0], check_points_WF_i[1], c='b') + #for i in range(1,len(self.conf.check_points_BF)): + # check_points_WF_i = np.array([[math.cos(theta), -math.sin(theta)], [math.sin(theta), math.cos(theta)]]).dot(self.conf.check_points_BF[i,:]) + ICS[0,:2] + # ax.scatter(check_points_WF_i[0], check_points_WF_i[1], c='r') + + ax.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5, legend='Goal position') + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.set_title('Plane') + #ax.legend() + ax.grid(True) + plt.show() + + def compute_ICS(self, p_ee, sys_id, theta=None, continue_flag=0): + if sys_id == 'manipulator': + radius = math.sqrt((p_ee[0]-self.conf.x_base)**2+(p_ee[1])**2) + if radius > 30: + continue_flag = 1 + return None, continue_flag + + phi = math.atan2(p_ee[1]-self.conf.y_base,(p_ee[0]-self.conf.x_base)) # SUM OF THE ANGLES FIXED + X3rd_joint = (p_ee[0]-self.conf.x_base) - self.conf.l* math.cos(phi) + Y3rd_joint = (p_ee[1]-self.conf.y_base) - self.conf.l* math.sin(phi) + + if abs(X3rd_joint) <= 1e-6 and abs(Y3rd_joint) <= 1e-6: + continue_flag = 1 + return None, continue_flag + + c2 = (X3rd_joint**2 + Y3rd_joint**2 -2*self.conf.l**2)/(2*self.conf.l**2) + + if p_ee[1] >= 0: + s2 = math.sqrt(1-c2**2) + else: + s2 = -math.sqrt(1-c2**2) + + s1 = ((self.conf.l + self.conf.l*c2)*Y3rd_joint - self.conf.l*s2*X3rd_joint)/(X3rd_joint**2 + Y3rd_joint**2) + c1 = ((self.conf.l + self.conf.l*c2)*X3rd_joint - self.conf.l*s2*Y3rd_joint)/(X3rd_joint**2 + Y3rd_joint**2) + ICS_q0 = math.atan2(s1,c1) + ICS_q1 = math.atan2(s2,c2) + ICS_q2 = phi-ICS_q0-ICS_q1 + + ICS = np.array([ICS_q0, ICS_q1, ICS_q2, 0.0, 0.0, 0.0, 0.0]) + + elif sys_id == 'car': + if theta == None: + theta = 0*np.random.uniform(-math.pi,math.pi) + ICS = np.array([p_ee[0], p_ee[1], theta, 0.0, 0.0, 0.0]) + + elif sys_id == 'car_park': + if theta == None: + #theta = 0*np.random.uniform(-math.pi,math.pi) + theta = np.pi/2 + ICS = np.array([p_ee[0], p_ee[1], theta, 0.0, 0.0, 0.0]) + + elif sys_id == 'double_integrator': + ICS = np.array([p_ee[0], p_ee[1], 0.0, 0.0, 0.0]) + + elif sys_id == 'single_integrator': + ICS = np.array([p_ee[0], p_ee[1], 0.0]) + + return ICS, continue_flag + + def plot_policy(self, tau, x, y, steps, n_updates, diff_loc=0): + ''' Plot policy rollout from a single initial state as well as state and control trajectories ''' + timesteps = self.self.conf.dt*np.arange(steps) + + fig = plt.figure(figsize=(12,8)) + plt.suptitle('POLICY: Discrete model, N try = {} N updates = {}'.format(self.N_try,n_updates), y=1) + + ax1 = fig.add_subplot(2, 2, 1) + ax1.plot(timesteps, x, 'ro', linewidth=1, markersize=1, legedn='x') + ax1.plot(timesteps, y, 'bo', linewidth=1, markersize=1, legend='y') + ax1.set_xlabel('Time [s]') + ax1.set_ylabel('[m]') + ax1.set_title('End-Effector Position') + ax1.legend() + ax1.grid(True) + + col = ['ro', 'bo', 'go'] + ax2 = fig.add_subplot(2, 2, self.conf.nb_action) + for i in range(self.conf.nb_action): + ax2.plot(timesteps, tau[:,i], col[i], linewidth=1, markersize=1,legend='tau{}'.format(i)) + ax2.set_xlabel('Time [s]') + ax2.set_title('Controls') + ax2.legend() + ax2.grid(True) + + ax3 = fig.add_subplot(1, 2, 2) + ax3.plot(x, y, 'ro', linewidth=1, markersize=1) + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax3.add_artist(obs_plot_list[i]) + ax3.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=10) + ax3.set_xlim(self.xlim) + ax3.set_ylim(self.ylim) + ax3.set_aspect('equal', 'box') + ax3.set_xlabel('X [m]') + ax3.set_ylabel('Y [m]') + ax3.set_title('Plane') + ax3.grid(True) + + fig.tight_layout() + + if diff_loc==0: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationSingleInit_{}_{}'.format(self.N_try,n_updates)) + else: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationMultiInit_{}_{}'.format(self.N_try,n_updates)) + + plt.clf() + plt.close(fig) + + def plot_policy_eval(self, p_list, n_updates, diff_loc=0, theta=0): + ''' Plot only policy rollouts from multiple initial states ''' + fig = plt.figure(figsize=(12,8)) + plt.suptitle('POLICY: Discrete model, N try = {} N updates = {}'.format(self.N_try,n_updates), y=1) + + ax = fig.add_subplot(1, 1, 1) + for idx in range(len(p_list)): + ax.plot(p_list[idx][:,0], p_list[idx][:,1], marker='o', linewidth=0.3, markersize=1) + ax.plot(p_list[idx][0,0],p_list[idx][0,1],'ko',markersize=5) + if self.conf.system_id == 'car_park': + theta = p_list[idx][-1,2] + fancybox = FancyBboxPatch((0 - self.conf.L/2, 0 - self.conf.W/2), self.conf.L, self.conf.W, edgecolor='none', alpha=0.5, boxstyle='round,pad=0') + fancybox.set_transform(Affine2D().rotate_deg(np.rad2deg(theta)).translate(p_list[idx][-1,0], p_list[idx][-1,1]) + ax.transData) + ax.add_patch(fancybox) + + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + + ax.plot(self.conf.TARGET_STATE[0],self.conf.TARGET_STATE[1],'b*',markersize=10) + + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.grid(True) + fig.tight_layout() + if diff_loc==0: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationSingleInit_{}_{}'.format(self.N_try,n_updates)) + else: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationMultiInit_{}_{}'.format(self.N_try,n_updates)) + + plt.clf() + plt.close(fig) + + def rollout(self,update_step_cntr, actor_model, init_states_sim, diff_loc=0): + ''' Plot rollout of the actor from some initial states. It generates the results and then calls plot_policy() and plot_policy_eval() ''' + #tau_all_sim = [] + p_ee_all_sim = [] + + returns = {} + + for k in range(len(init_states_sim)): + rollout_controls = np.zeros((self.conf.NSTEPS,self.conf.nb_action)) + rollout_states = np.zeros((self.conf.NSTEPS+1,self.conf.nb_state)) + rollout_p_ee = np.zeros((self.conf.NSTEPS+1,3)) + rollout_episodic_reward = 0 + + rollout_p_ee[0,:] = self.env.get_end_effector_position(init_states_sim[k]) + rollout_states[0,:] = np.copy(init_states_sim[k]) + + for i in range(self.conf.NSTEPS): + rollout_controls[i,:] = tf.squeeze(self.NN.eval(actor_model, np.array([rollout_states[i,:]]))).numpy() + rollout_states[i+1,:], rwrd_sim = self.env.step(self.conf.cost_weights_running, rollout_states[i,:],rollout_controls[i,:]) + rollout_p_ee[i+1,:] = self.env.get_end_effector_position(rollout_states[i+1,:]) + + rollout_p_ee[i+1,-1] = rollout_states[i+1,2] ### !!! ### + + rollout_episodic_reward += rwrd_sim + + if k==0: + print("N try = {}: Simulation Return @ N updates = {} ==> {}".format(self.N_try,update_step_cntr,rollout_episodic_reward)) + + p_ee_all_sim.append(rollout_p_ee) + + returns[init_states_sim[k][0],init_states_sim[k][1]] = rollout_episodic_reward + + self.plot_policy_eval(p_ee_all_sim,update_step_cntr, diff_loc=diff_loc) + + return returns + + def plot_results(self, tau, ee_pos_TO, ee_pos_RL, steps, to=0): + ''' Plot results from TO and episode to check consistency ''' + timesteps = self.conf.dt*np.arange(steps+1) + fig = plt.figure(figsize=(12,8)) + if to: + plt.suptitle('TO EXPLORATION: N try = {}'.format(self.N_try), y=1, fontsize=20) + else: + plt.suptitle('POLICY EXPLORATION: N try = {}'.format(self.N_try), y=1, fontsize=20) + + ax1 = fig.add_subplot(2, 2, 1) + ax1.plot(timesteps, ee_pos_TO[:,0], 'ro', linewidth=1, markersize=1,legend="x_TO") + ax1.plot(timesteps, ee_pos_TO[:,1], 'bo', linewidth=1, markersize=1,legend="y_TO") + ax1.plot(timesteps, ee_pos_RL[:,0], 'go', linewidth=1, markersize=1,legend="x_RL") + ax1.plot(timesteps, ee_pos_RL[:,1], 'ko', linewidth=1, markersize=1,legend="y_RL") + ax1.set_xlabel('Time [s]') + ax1.set_ylabel('[m]') + ax1.set_title('End-Effector Position') + ax1.set_xlim(0, timesteps[-1]) + ax1.legend() + ax1.grid(True) + + ax2 = fig.add_subplot(2, 2, 3) + col = ['ro', 'bo', 'go'] + for i in range(self.conf.nb_action): + ax2.plot(timesteps[:-1], tau[:,i], col[i], linewidth=1, markersize=1,legend='tau{}'.format(i)) + ax2.set_xlabel('Time [s]') + ax2.set_title('Controls') + ax2.legend() + ax2.grid(True) + + ax3 = fig.add_subplot(1, 2, 2) + ax3.plot(ee_pos_TO[:,0], ee_pos_TO[:,1], 'ro', linewidth=1, markersize=2,legend='TO') + ax3.plot(ee_pos_RL[:,0], ee_pos_RL[:,1], 'bo', linewidth=1, markersize=2,legend='RL') + ax3.plot([ee_pos_TO[0,0]],[ee_pos_TO[0,1]],'ro',markersize=5) + ax3.plot([ee_pos_RL[0,0]],[ee_pos_RL[0,1]],'bo',markersize=5) + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax3.add_artist(obs_plot_list[i]) + ax3.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax3.set_xlim(self.xlim) + ax3.set_ylim(self.ylim) + ax3.set_aspect('equal', 'box') + ax3.set_xlabel('X [m]') + ax3.set_ylabel('Y [m]') + ax3.set_title('Plane') + ax3.legend() + ax3.grid(True) + + fig.tight_layout() + #plt.show() + + def plot_Return(self, ep_reward_list): + ''' Plot returns (not so meaningful given that the initial state, so also the time horizon, of each episode is randomized) ''' + fig = plt.figure(figsize=(15,8)) + ax = fig.add_subplot(1, 1, 1) + ax.set_yscale('log') + ax.plot(ep_reward_list**2) + ax.set_xlabel("Episode") + ax.set_ylabel("Return") + ax.set_title("N_try = {}".format(self.N_try)) + ax.grid(True) + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/EpReturn_{}'.format(self.N_try)) + plt.close() + + def plot_Critic_Value_function(self, critic_model, n_update, sys_id, name='V'): + ''' Plot Value function as learned by the critic ''' + if sys_id == 'manipulator': + N_discretization_x = 60 + 1 + N_discretization_y = 60 + 1 + + plot_data = np.zeros(N_discretization_y*N_discretization_x)*np.nan + ee_pos = np.zeros((N_discretization_y*N_discretization_x,3))*np.nan + + for k_x in range(N_discretization_x): + for k_y in range(N_discretization_y): + ICS = self.env.reset() + ICS[-1] = 0 + ee_pos[k_x*(N_discretization_y)+k_y,:] = self.env.get_end_effector_position(ICS) + plot_data[k_x*(N_discretization_y)+k_y] = self.NN.eval(critic_model, np.array([ICS])) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot() + plt.scatter(ee_pos[:,0], ee_pos[:,1], c=plot_data, cmap=cm.coolwarm, antialiased=False) + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + plt.colorbar() + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/{}_{}'.format(self.conf.Fig_path,self.N_try,name,int(n_update))) + plt.close() + + else: + N_discretization_x = 30 + 1 + N_discretization_y = 30 + 1 + + plot_data = np.zeros((N_discretization_y,N_discretization_x))*np.nan + + ee_x = np.linspace(-15, 15, N_discretization_x) + ee_y = np.linspace(-15, 15, N_discretization_y) + + for k_y in range(N_discretization_y): + for k_x in range(N_discretization_x): + p_ee = np.array([ee_x[k_x], ee_y[k_y], 0]) + ICS, continue_flag = self.compute_ICS(p_ee, sys_id, continue_flag=0) + if continue_flag: + continue + plot_data[k_x,k_y] = self.NN.eval(critic_model, np.array([ICS])) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot() + plt.contourf(ee_x, ee_y, plot_data.T, cmap=cm.coolwarm, antialiased=False) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + plt.colorbar() + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/{}_{}'.format(self.conf.Fig_path,self.N_try,name,int(n_update))) + plt.close() + + def plot_Critic_Value_function_from_sample(self, n_update, NSTEPS_SH, state_arr, reward_arr): + # Store transition after computing the (partial) cost-to go when using n-step TD (from 0 to Monte Carlo) + reward_to_go_arr = np.zeros(sum(NSTEPS_SH)+len(NSTEPS_SH)*1) + idx = 0 + for n in range(len(NSTEPS_SH)): + for i in range(NSTEPS_SH[n]+1): + # Compute the partial cost to go + reward_to_go_arr[idx] = sum(reward_arr[n][i:]) + idx += 1 + + state_arr = np.concatenate(state_arr, axis=0) + ee_pos_arr = np.zeros((len(state_arr),3)) + for i in range(state_arr.shape[0]): + ee_pos_arr[i,:] = self.env.get_end_effector_position(state_arr[i]) + + + mi = min(reward_to_go_arr) + ma = max(reward_to_go_arr) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot()#projection='3d') + norm = colors.Normalize(vmin=mi,vmax=ma) + + obs_plot_list = self.plot_obstaces(a=0.5) + + ax.scatter(ee_pos_arr[:,0],ee_pos_arr[:,1], c=reward_to_go_arr, norm=norm, cmap=cm.coolwarm, marker='x') + + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + + plt.colorbar(cm.ScalarMappable(norm=norm,cmap=cm.coolwarm)) + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/V_sample_{}'.format(self.conf.Fig_path,self.N_try,int(n_update))) + plt.close() + + def plot_ICS(self,state_arr): + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + for j in range(len(state_arr)): + ax.scatter(state_arr[j][0,0],state_arr[j][0,1]) + obs_plot_list = plot_fun.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.fig_ax_lim[0].tolist()) + ax.set_ylim(self.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/ICS_{}_S{}'.format(conf.Fig_path,N_try,update_step_counter,int(w_S))) + plt.close(fig) + + def plot_rollout_and_traj_from_ICS(self, init_state, n_update, actor_model, TrOp, tag, steps=200): + ''' Plot results from TO and episode to check consistency ''' + colors = cm.coolwarm(np.linspace(0.1,1,len(init_state))) + + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + + for j in range(len(init_state)): + + ee_pos_TO = np.zeros((steps,3)) + ee_pos_RL = np.zeros((steps,3)) + + RL_states = np.zeros((steps,self.conf.nb_state)) + RL_action = np.zeros((steps-1,self.conf.nb_action)) + RL_states[0,:] = init_state[j,:] + ee_pos_RL[0,:] = self.env.get_end_effector_position(RL_states[0,:]) + + for i in range(steps-1): + RL_action[i,:] = self.NN.eval(actor_model, np.array([RL_states[i,:]])) + RL_states[i+1,:] = self.env.simulate(RL_states[i,:], RL_action[i,:]) + ee_pos_RL[i+1,:] = self.env.get_end_effector_position(RL_states[i+1,:]) + + TO_states, _ = TrOp.TO_System_Solve3(init_state[j,:], RL_states.T, RL_action.T, steps-1) + + try: + for i in range(steps): + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[i,:]) + except: + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[0,:]) + + ax.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax.scatter(ee_pos_TO[0,0],ee_pos_TO[0,1],color=colors[j]) + ax.scatter(ee_pos_RL[0,0],ee_pos_RL[0,1],color=colors[j]) + ax.plot(ee_pos_TO[1:,0],ee_pos_TO[1:,1],color=colors[j]) + ax.plot(ee_pos_RL[1:,0],ee_pos_RL[1:,1],'--',color=colors[j]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.set_title('Plane') + #ax.legend() + ax.grid(True) + + plt.savefig('{}/N_try_{}/ee_traj_{}_{}'.format(self.conf.Fig_path,self.N_try,int(n_update), tag)) + + def plot_ICS(self, input_arr, cs=0): + if cs == 1: + p_arr = np.zeros((len(input_arr),3)) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + for j in range(len(input_arr)): + p_arr[j,:] = input_arr[j,:] + ax.scatter(p_arr[:,0],p_arr[:,1]) + obs_plot_list = self.plot_obstaces(a = 0.5) + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.conf.fig_ax_lim[0].tolist()) + ax.set_ylim(self.conf.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + ax.grid() + plt.savefig('{}/N_try_{}/ICS'.format(self.conf.Fig_path,self.N_try)) + plt.close(fig) + else: + p_arr = np.zeros((len(input_arr),3)) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + + for j in range(len(input_arr)): + p_arr[j,:] = self.env.get_end_effector_position(input_arr[j]) + ax.scatter(p_arr[:,0],p_arr[:,1]) + obs_plot_list = self.plot_obstaces(a = 0.5) + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.conf.fig_ax_lim[0].tolist()) + ax.set_ylim(self.conf.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + ax.grid() + plt.savefig('{}/N_try_{}/ICS'.format(self.conf.Fig_path,self.N_try)) + plt.close(fig) + + def plot_traj_from_ICS(self, init_state, TrOp, RLAC, update_step_counter=0,ep=0,steps=200, init=0,continue_flag=1): + ''' Plot results from TO and episode to check consistency ''' + colors = cm.coolwarm(np.linspace(0.1,1,len(init_state))) + + fig = plt.figure(figsize=(12,8)) + ax1 = fig.add_subplot(1,2,1) + ax2 = fig.add_subplot(1,2,2) + + for j in range(len(init_state)): + + ee_pos_TO = np.zeros((steps,3)) + ee_pos_RL = np.zeros((steps,3)) + + if init == 0: + # zeros + _, init_TO_states, init_TO_controls, _, success_init_flag = RLAC.create_TO_init(0, init_state[j,:]) + elif init == 1: + # NN + _, init_TO_states, init_TO_controls, _, success_init_flag = RLAC.create_TO_init(1, init_state[j,:]) + + if success_init_flag: + _, _, TO_states, _, _, _ = TrOp.TO_System_Solve(init_state[j,:], init_TO_states, init_TO_controls, steps-1) + else: + continue + + try: + for i in range(steps): + ee_pos_RL[i,:] = self.env.get_end_effector_position(init_TO_states[i,:]) + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[i,:]) + except: + ee_pos_RL[i,:] = self.env.get_end_effector_position(init_TO_states[0,:]) + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[0,:]) + + ax1.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax1.scatter(ee_pos_RL[0,0],ee_pos_RL[0,1],color=colors[j]) + ax1.plot(ee_pos_RL[1:,0],ee_pos_RL[1:,1],'--',color=colors[j]) + + ax2.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax2.scatter(ee_pos_TO[0,0],ee_pos_TO[0,1],color=colors[j]) + ax2.plot(ee_pos_TO[1:,0],ee_pos_TO[1:,1],color=colors[j]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax1.add_patch(obs_plot_list[i]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax2.add_patch(obs_plot_list[i]) + + ax1.set_xlim(self.xlim) + ax1.set_ylim(self.ylim) + ax1.set_aspect('equal', 'box') + ax1.set_xlabel('X [m]') + ax1.set_ylabel('Y [m]') + ax1.set_title('Warmstart traj.') + + ax2.set_xlim(self.xlim) + ax2.set_ylim(self.ylim) + ax2.set_aspect('equal', 'box') + ax2.set_xlabel('X [m]') + #ax2.set_ylabel('Y [m]') + ax2.set_title('TO traj.') + #ax.legend() + ax1.grid(True) + ax2.grid(True) + + plt.savefig('{}/N_try_{}/ee_traj_{}_{}'.format(self.conf.Fig_path,self.N_try,init,update_step_counter)) + + + + +if __name__ == '__main__': + import os + import sys + import time + import random + import importlib + import numpy as np + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # {'0' -> show all logs, '1' -> filter out info, '2' -> filter out warnings} + import tensorflow as tf + import matplotlib.pyplot as plt + import mpl_toolkits.mplot3d.art3d as art3d + + from RL import RL_AC + from plot_utils import PLOT + from NeuralNetwork import NN + + ### Input ### + N_try = 0 + + seed = 0 + tf.random.set_seed(seed) # Set tensorflow seed + random.seed(seed) # Set random seed + + system_id = 'car_park' + + TO_method = 'casadi' + + recover_training_flag = 0 + + CPU_flag = 0 + if CPU_flag: + os.environ["CUDA_VISIBLE_DEVICES"]="-1" + tf.config.experimental.list_physical_devices('GPU') + + nb_cpus = 1 + + w_S = 0 + ################################# + + # Import configuration file and environment file + system_map = { + 'single_integrator': ('conf_single_integrator', 'SingleIntegrator'), + 'double_integrator': ('conf_double_integrator', 'DoubleIntegrator'), + 'car': ('conf_car', 'Car'), + 'car_park': ('conf_car_park', 'CarPark'), + 'manipulator': ('conf_manipulator', 'Manipulator'), + 'ur5': ('conf_ur5', 'UR5') + } + + try: + conf_module, env_class = system_map[system_id] + conf = importlib.import_module(conf_module) + Environment = getattr(importlib.import_module('environment'), env_class) + except KeyError: + print('System {} not found'.format(system_id)) + sys.exit() + + + + # Create folders to store the results and the trained NNs and save configuration + for path in conf.path_list: + os.makedirs(path + '/N_try_{}'.format(N_try), exist_ok=True) + os.makedirs(conf.Config_path, exist_ok=True) + + params = [p for p in conf.__dict__ if not p.startswith("__")] + with open(conf.Config_path + '/config{}.txt'.format(N_try), 'w') as f: + for p in params: + f.write('{} = {}\n'.format(p, conf.__dict__[p])) + f.write('Seed = {}\n'.format(seed)) + f.write('w_S = {}'.format(w_S)) + + + + # Create environment instances + env = Environment(conf) + + # Create NN instance + NN_inst = NN(env, conf, w_S) + + # Create RL_AC instance + RLAC = RL_AC(env, NN_inst, conf, N_try) + + # Set initial weights of the NNs, initialize the counter of the updates and setup NN models + if recover_training_flag: + recover_training = np.array([conf.NNs_path_rec, conf.N_try_rec, conf.update_step_counter_rec]) + update_step_counter = conf.update_step_counter_rec + nb_starting_episode = (conf.update_step_counter_rec/conf.UPDATE_LOOPS)+1 + + RLAC.setup_model(recover_training) + else: + update_step_counter = 0 + nb_starting_episode = 0 + + RLAC.setup_model() + + # Create PLOT instance + plot_fun = PLOT(N_try, env, NN_inst, conf) + + plot_fun.plot_Reward() \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_6/replay_buffer.py b/Results Double Integrator/Results set test/Code/N_try_6/replay_buffer.py new file mode 100644 index 0000000..ba1804d --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/replay_buffer.py @@ -0,0 +1,240 @@ +import math +import random +import numpy as np +import tensorflow as tf +import segment_tree +#from stable_baselines.common.segment_tree import SumSegmentTree, MinSegmentTree + + +class ReplayBuffer(object): + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param BATCH_SIZE : (int) Size of the mini-batch + :param nb_state : (int) State size (robot state size + 1) + ''' + + self.conf = conf + self.storage_mat = np.zeros((conf.REPLAY_SIZE, conf.nb_state + 1 + conf.nb_state + conf.nb_state + 1 + 1)) + self.next_idx = 0 + self.full = 0 + self.exp_counter = np.zeros(conf.REPLAY_SIZE) + + def add(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Add transitions to the buffer ''' + data = self.concatenate_sample(obses_t, rewards, obses_t1, dVdxs, dones, terms) + + if len(data) + self.next_idx > self.conf.REPLAY_SIZE: + self.storage_mat[self.next_idx:,:] = data[:self.conf.REPLAY_SIZE-self.next_idx,:] + self.storage_mat[:self.next_idx+len(data)-self.conf.REPLAY_SIZE,:] = data[self.conf.REPLAY_SIZE-self.next_idx:,:] + self.full = 1 + else: + self.storage_mat[self.next_idx:self.next_idx+len(data),:] = data + + self.next_idx = (self.next_idx + len(data)) % self.conf.REPLAY_SIZE + + def sample(self): + ''' Sample a batch of transitions ''' + # Select indexes of the batch elements + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + idxes = np.random.randint(0, max_idx, size=self.conf.BATCH_SIZE) + + obses_t = self.storage_mat[idxes, :self.conf.nb_state] + rewards = self.storage_mat[idxes, self.conf.nb_state:self.conf.nb_state+1] + obses_t1 = self.storage_mat[idxes, self.conf.nb_state+1:self.conf.nb_state*2+1] + dVdxs = self.storage_mat[idxes, self.conf.nb_state*2+1:self.conf.nb_state*3+1] + dones = self.storage_mat[idxes, self.conf.nb_state*3+1:self.conf.nb_state*3+2] + terms = self.storage_mat[idxes, self.conf.nb_state*3+2:self.conf.nb_state*3+3] + + # Priorities not used + weights = np.ones((self.conf.BATCH_SIZE,1)) + batch_idxes = None + + # Convert the sample in tensor + obses_t, rewards, obses_t1, dVdxs, dones, weights = self.convert_sample_to_tensor(obses_t, rewards, obses_t1, dVdxs, dones, weights) + + return obses_t, rewards, obses_t1, dVdxs, dones, terms, weights, batch_idxes + + def concatenate_sample(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Convert batch of transitions into a tensor ''' + obses_t = np.concatenate(obses_t, axis=0) + rewards = np.concatenate(rewards, axis=0) + obses_t1 = np.concatenate(obses_t1, axis=0) + dVdxs = np.concatenate(dVdxs, axis=0) + dones = np.concatenate(dones, axis=0) + terms = np.concatenate(terms, axis=0) + + return np.concatenate((obses_t, rewards.reshape(-1,1), obses_t1, dVdxs, dones.reshape(-1,1), terms.reshape(-1,1)),axis=1) + + def convert_sample_to_tensor(self, obses_t, rewards, obses_t1, dVdxs, dones, weights): + ''' Convert batch of transitions into a tensor ''' + obses_t = tf.convert_to_tensor(obses_t, dtype=tf.float32) + rewards = tf.convert_to_tensor(rewards, dtype=tf.float32) + obses_t1 = tf.convert_to_tensor(obses_t1, dtype=tf.float32) + dVdxs = tf.convert_to_tensor(dVdxs, dtype=tf.float32) + dones = tf.convert_to_tensor(dones, dtype=tf.float32) + weights = tf.convert_to_tensor(weights, dtype=tf.float32) + + return obses_t, rewards, obses_t1, dVdxs, dones, weights + + + +class PrioritizedReplayBuffer: + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param BATCH_SIZE : (int) Size of the mini-batch + :param nb_state : (int) State size (robot state size + 1) + :param prioritized_replay_alpha : (float) Determines how much prioritization is used, set to 0 to use a normal buffer + :param prioritized_replay_beta : (float) Small positive constant that prevents the edge-case of transitions not being revisited once their error is zero + ''' + + self.conf = conf + + self.storage_mat = np.zeros((conf.REPLAY_SIZE, conf.nb_state + 1 + conf.nb_state + conf.nb_state + 1 + 1)) + self.full = 0 + self.next_idx = 0 + self.exp_counter = np.zeros(self.conf.REPLAY_SIZE) + self.priorities = np.empty(self.conf.REPLAY_SIZE) + + assert conf.prioritized_replay_alpha >= 0 + assert conf.prioritized_replay_beta > 0 + + it_capacity = 1 + while it_capacity < self.conf.REPLAY_SIZE: + it_capacity *= 2 + + self._it_sum = SumSegmentTree(it_capacity) + self._it_min = MinSegmentTree(it_capacity) + self._max_priority = 1.0 + + #self.RB_type = 'ReLO' + + self.MSE = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE) + + def add(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Add transitions to the buffer ''' + data = self.concatenate_sample(obses_t, rewards, obses_t1, dVdxs, dones, terms) + + if len(data) + self.next_idx > self.conf.REPLAY_SIZE: + self.storage_mat[self.next_idx:,:] = data[:self.conf.REPLAY_SIZE-self.next_idx,:] + self.storage_mat[:self.next_idx+len(data)-self.conf.REPLAY_SIZE,:] = data[self.conf.REPLAY_SIZE-self.next_idx:,:] + self.full = 1 + else: + self.storage_mat[self.next_idx:self.next_idx+len(data),:] = data + + for i in range(len(data)): + self._it_sum[(self.next_idx+i) % self.conf.REPLAY_SIZE] = self._max_priority ** self.conf.prioritized_replay_alpha + self._it_min[(self.next_idx+i) % self.conf.REPLAY_SIZE] = self._max_priority ** self.conf.prioritized_replay_alpha + + self.next_idx = (self.next_idx + len(data)) % self.conf.REPLAY_SIZE + + def _sample_proportional(self): + ''' Sample a batch of transitions ''' + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + + idx_arr = np.zeros(self.conf.BATCH_SIZE) + + p_total = self._it_sum.sum(0, max_idx - 1) + + segment = p_total / self.conf.BATCH_SIZE + + for i in range(self.conf.BATCH_SIZE): + p = random.random() * segment + i * segment #random.random(segment*i, segment*(i+1)) + idx = self._it_sum.find_prefixsum_idx(p) + idx_arr[i] = idx + + return idx_arr + + def sample(self): + ''' Sample a batch of transitions ''' + # Select indexes of the batch elements + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + + idxes = self._sample_proportional() + batch_idxes = idxes.astype(int) + + # Compute weights normalization + p_min = self._it_min.min() / self._it_sum.sum() + max_weight = (p_min * max_idx) ** (-self.conf.prioritized_replay_beta) + + self.exp_counter[batch_idxes] += 1 + self.priorities[batch_idxes] = self._it_sum[batch_idxes] / self._it_sum.sum() + weights = (self.priorities[batch_idxes] * max_idx) ** (-self.conf.prioritized_replay_beta) / max_weight + + obses_t = self.storage_mat[batch_idxes, :self.conf.nb_state] + rewards = self.storage_mat[batch_idxes, self.conf.nb_state:self.conf.nb_state+1] + obses_t1 = self.storage_mat[batch_idxes, self.conf.nb_state+1:self.conf.nb_state*2+1] + dVdxs = self.storage_mat[batch_idxes, self.conf.nb_state*2+1:self.conf.nb_state*3+1] + dones = self.storage_mat[batch_idxes, self.conf.nb_state*3+1:self.conf.nb_state*3+2] + terms = self.storage_mat[batch_idxes, self.conf.nb_state*3+2:self.conf.nb_state*3+3] + + # Convert the sample in tensor + obses_t, rewards, obses_t1, dVdxs, dones, weights = self.convert_sample_to_tensor(obses_t, rewards, obses_t1, dVdxs, dones, weights) + + return obses_t, rewards, obses_t1, dVdxs, dones, terms, weights, batch_idxes + + def update_priorities(self, idxes, reward_to_go_batch, critic_value, target_critic_value=None): + '''Update priorities of sampled transitions ''' + # Create td_errors + if self.RB_type == 'ReLO': + # MSE(Delta V_c) - MSE(Delta V_t) + td_errors = self.MSE(reward_to_go_batch, critic_value).numpy()-self.MSE(reward_to_go_batch, target_critic_value).numpy() + td_errors_norm = np.clip(td_errors, 0, np.max(td_errors)) + else: # 'PER' is default self.RB_type + # |TD_error_i| + td_errors_norm = tf.math.abs(tf.math.subtract(reward_to_go_batch, critic_value)) + td_errors_norm = td_errors_norm[:,0] + + # Compute the freshness discount factor + fresh_disc_factor = self.conf.fresh_factor**self.exp_counter[idxes] + + # Compute new priorities: p_i = mu**C_i * td_error + self.conf.prioritized_replay_eps + new_priorities = fresh_disc_factor * td_errors_norm + self.conf.prioritized_replay_eps + + # Sets priority of transition at index idxes[i] in buffer to priorities[i] + assert len(idxes) == len(new_priorities) + for idx, priority in zip(idxes, new_priorities): + assert priority > 0 + + idx = int(idx) + + self._it_sum[idx] = priority ** self.conf.prioritized_replay_alpha + self._it_min[idx] = priority ** self.conf.prioritized_replay_alpha + + self._max_priority = max(self._max_priority, priority) + + def concatenate_sample(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Convert batch of transitions into a tensor ''' + obses_t = np.concatenate(obses_t, axis=0) + rewards = np.concatenate(rewards, axis=0) + obses_t1 = np.concatenate(obses_t1, axis=0) + dVdxs = np.concatenate(dVdxs, axis=0) + dones = np.concatenate(dones, axis=0) + terms = np.concatenate(terms, axis=0) + + return np.concatenate((obses_t, rewards.reshape(-1,1), obses_t1, dVdxs, dones.reshape(-1,1), terms.reshape(-1,1)),axis=1) + + def convert_sample_to_tensor(self, obses_t, rewards, obses_t1, dVdxs, dones, weights): + ''' Convert batch of transitions into a tensor ''' + obses_t = tf.convert_to_tensor(obses_t, dtype=tf.float32) + rewards = tf.convert_to_tensor(rewards, dtype=tf.float32) + obses_t1 = tf.convert_to_tensor(obses_t1, dtype=tf.float32) + dVdxs = tf.convert_to_tensor(dVdxs, dtype=tf.float32) + dones = tf.convert_to_tensor(dones, dtype=tf.float32) + weights = tf.convert_to_tensor(weights, dtype=tf.float32) + + return obses_t, rewards, obses_t1, dVdxs, dones, weights diff --git a/Results Double Integrator/Results set test/Code/N_try_6/robot_utils.py b/Results Double Integrator/Results set test/Code/N_try_6/robot_utils.py new file mode 100644 index 0000000..d3cc937 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/robot_utils.py @@ -0,0 +1,637 @@ +import os +import math +import random +#import gepetto#.corbaserver +import time +import subprocess +import numpy as np +import pinocchio as pin +from numpy.linalg import norm +from pinocchio.robot_wrapper import RobotWrapper as PinocchioRobotWrapper + +class Contact: + ''' A contact between a contact-point and a contact-surface + ''' + def __init__(self, contact_point, contact_surface): + self.cp = contact_point + self.cs = contact_surface + self.reset_contact_position() + + def reset_contact_position(self): + # Initialize anchor point p0, that is the initial (0-load) position of the spring + self.p0 = self.cp.get_position() + self.in_contact = True + + def compute_force(self): + self.f, self.p0 = self.cs.compute_force(self.cp, self.p0) + return self.f + + def get_jacobian(self): + return self.cp.get_jacobian() + +class RobotWrapper(PinocchioRobotWrapper): + + @staticmethod + def BuildFromURDF(filename, package_dirs=None, root_joint=None, verbose=False, meshLoader=None): + robot = RobotWrapper() + robot.initFromURDF(filename, package_dirs, root_joint, verbose, meshLoader) + return robot + + @property + def na(self): + if(self.model.joints[0].nq==7): + return self.model.nv-6 + return self.model.nv + + def mass(self, q, update=True): + if(update): + return pin.crba(self.model, self.data, q) + return self.data.M + + def nle(self, q, v, update=True): + if(update): + return pin.nonLinearEffects(self.model, self.data, q, v) + return self.data.nle + + def com(self, q=None, v=None, a=None, update=True): + if(update==False or q is None): + return PinocchioRobotWrapper.com(self, q) + if a is None: + if v is None: + return PinocchioRobotWrapper.com(self, q) + return PinocchioRobotWrapper.com(self, q, v) + return PinocchioRobotWrapper.com(self, q, v,a) + + def Jcom(self, q, update=True): + if(update): + return pin.jacobianCenterOfMass(self.model, self.data, q) + return self.data.Jcom + + def momentumJacobian(self, q, v, update=True): + if(update): + pin.ccrba(self.model, self.data, q, v) + return self.data.Ag + + def computeAllTerms(self, q, v): + ''' pin.computeAllTerms is equivalent to calling: + pinocchio::forwardKinematics + pinocchio::crba + pinocchio::nonLinearEffects + pinocchio::computeJointJacobians + pinocchio::centerOfMass + pinocchio::jacobianCenterOfMass + pinocchio::kineticEnergy + pinocchio::potentialEnergy + This is too much for our needs, so we call only the functions + we need, including those for the frame kinematics + ''' + #pin.computeAllTerms(self.model, self.data, q, v); + pin.forwardKinematics(self.model, self.data, q, v, np.zeros(self.model.nv)) + pin.computeJointJacobians(self.model, self.data) + pin.updateFramePlacements(self.model, self.data) + pin.crba(self.model, self.data, q) + pin.nonLinearEffects(self.model, self.data, q, v) + + def forwardKinematics(self, q, v=None, a=None): + if v is not None: + if a is not None: + pin.forwardKinematics(self.model, self.data, q, v, a) + else: + pin.forwardKinematics(self.model, self.data, q, v) + else: + pin.forwardKinematics(self.model, self.data, q) + + def inverseKinematics(self, ee_coords): + ''' Inverse kinematics algorithm to compute a joint configuration given the EE coordinates''' + + oMdes = pin.SE3(np.eye(3), np.array([ee_coords[0], ee_coords[1], 0.0])) + random.seed(123) + for j in range(100): + q = np.array([random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi)]) + # q = pin.neutral(self.model) + + pin.forwardKinematics(self.model, self.data, q) + pin.updateFramePlacements(self.model, self.data) + eps = 1e-4 + IT_MAX = 1000 + DT = 1e-1 + damp = 1e-12 + frame_id = self.model.getFrameId('fixed_ee') + i=0 + while True: + pin.forwardKinematics(self.model,self.data,q) + pin.updateFramePlacements(self.model, self.data) + dMi = oMdes.actInv(self.data.oMf[frame_id]) + err = pin.log(dMi).vector + if norm(err) < eps: + success = True + break + if i >= IT_MAX: + success = False + break + J = pin.computeFrameJacobian(self.model,self.data,q,frame_id) + v = - J.T.dot(np.linalg.solve(J.dot(J.T) + damp * np.eye(6), err)) + q = pin.integrate(self.model,q,v*DT) + if not i % 10: + print('%d: error = %s' % (i, err.T)) + i += 1 + + if success: + print("Convergence achieved!") + break + else: + print("\nWarning: the iterative algorithm has not reached convergence to the desired precision. Retry ({}) with another initial configuration".format(j)) + + return q, success + + def frameJacobian(self, q, index, update=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + ''' Call computeFrameJacobian if update is true. If not, user should call computeFrameJacobian first. + Then call getFrameJacobian and return the Jacobian matrix. + ref_frame can be: ReferenceFrame.LOCAL, ReferenceFrame.WORLD, ReferenceFrame.LOCAL_WORLD_ALIGNED + ''' + if(update): + pin.computeFrameJacobian(self.model, self.data, q, index) + return pin.getFrameJacobian(self.model, self.data, index, ref_frame) + + def frameVelocity(self, q, v, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v) + v_local = pin.getFrameVelocity(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return v_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + v_world = H.act(v_local) + return v_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + v = Hr.act(v_local) + return v + + def frameAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def frameClassicAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + v = pin.getFrameVelocity(self.model, self.data, index) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + a_local.linear += np.cross(v.angular, v.linear, axis=0) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def deactivateCollisionPairs(self, collision_pair_indexes): + for i in collision_pair_indexes: + self.collision_data.deactivateCollisionPair(i) + + def addAllCollisionPairs(self): + self.collision_model.addAllCollisionPairs() + self.collision_data = pin.GeometryData(self.collision_model) + + def isInCollision(self, q, stop_at_first_collision=True): + return pin.computeCollisions(self.model, self.data, self.collision_model, self.collision_data, np.asmatrix(q).reshape((self.model.nq,1)), stop_at_first_collision) + + def findFirstCollisionPair(self, consider_only_active_collision_pairs=True): + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + return (i, self.collision_model.collisionPairs[i]) + return None + + def findAllCollisionPairs(self, consider_only_active_collision_pairs=True): + res = [] + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + res += [(i, self.collision_model.collisionPairs[i])] + return res + +class RobotSimulator: + + def __init__(self, robot, q0_init, v0_init, simulation_type, tau_coulomb_max, use_viewer=False, DISPLAY_T=None, CAMERA_TRANSFORM=None, show_floor=False): + self.robot = robot + self.model = self.robot.model + self.data = self.model.createData() + self.t = 0.0 # time + self.nv = nv = self.model.nv # Dimension of joint velocities vector + self.na = na = robot.na # number of actuated joints + self.S = np.hstack((np.zeros((na, nv-na)), np.eye(na, na))) # Matrix S used as filter of vector of inputs U + + self.contacts = [] + self.candidate_contact_points = [] # candidate contact points + self.contact_surfaces = [] + + self.DISPLAY_T = DISPLAY_T # refresh period for viewer + self.use_viewer = use_viewer + self.tau_coulomb_max = tau_coulomb_max + self.display_counter = self.DISPLAY_T + self.init(q0_init, v0_init, True) + + self.tau_c = np.zeros(na) # Coulomb friction torque + self.simulation_type = simulation_type + self.set_coulomb_friction(tau_coulomb_max) + + # for gepetto viewer + self.gui = None + if(use_viewer): + try: + prompt = subprocess.getstatusoutput("ps aux |grep 'gepetto-gui'|grep -v 'grep'|wc -l") + if int(prompt[1]) == 0: + os.system('gepetto-gui &') + time.sleep(1) + except: + pass + gepetto.corbaserver.Client() + gepetto.Client() + self.robot.initViewer(loadModel=True) + self.gui = self.robot.viewer.gui + if(show_floor): + self.robot.viewer.gui.createSceneWithFloor('world') + self.gui.setLightingMode('world/floor', 'OFF') + self.robot.displayCollisions(False) + self.robot.displayVisuals(True) + self.robot.display(self.q) + try: + self.gui.setCameraTransform("python-pinocchio", CAMERA_TRANSFORM) + except: + self.gui.setCameraTransform(0, CAMERA_TRANSFORM) + + # Re-initialize the simulator + def init(self, q0=None, v0=None, reset_contact_positions=False): + self.first_iter = True + + if q0 is not None: + self.q = q0.copy() + + if(v0 is None): + self.v = np.zeros(self.robot.nv) + else: + self.v = v0.copy() + self.dv = np.zeros(self.robot.nv) + self.resize_contact_data(reset_contact_positions) + + def resize_contact_data(self, reset_contact_positions=False): + self.nc = len(self.contacts) # number of contacts + self.nk = 3*self.nc # size of contact force vector + self.f = np.zeros(self.nk) # contact forces + self.Jc = np.zeros((self.nk, self.model.nv)) # contact Jacobian + + # reset contact position + if(reset_contact_positions): + pin.forwardKinematics(self.model, self.data, self.q) + pin.updateFramePlacements(self.model, self.data) + for c in self.contacts: + c.reset_contact_position() + + self.compute_forces(compute_data=True) + + def set_coulomb_friction(self, tau_max): + self.tau_coulomb_max = 1e-2*tau_max*self.model.effortLimit + self.simulate_coulomb_friction = (norm(self.tau_coulomb_max)!=0.0) + + def collision_detection(self): + for s in self.contact_surfaces: # for each contact surface + for cp in self.candidate_contact_points: # for each candidate contact point + p = cp.get_position() + if(s.check_collision(p)): # check whether the point is colliding with the surface + if(not cp.active): # if the contact was not already active + print("Collision detected between point", cp.frame_name, " at ", p) + cp.active = True + cp.contact = Contact(cp, s) + self.contacts += [cp.contact] + self.resize_contact_data() + else: + if(cp.active): + print("Contact lost between point", cp.frame_name, " at ", p) + cp.active = False + self.contacts.remove(cp.contact) + self.resize_contact_data() + + def compute_forces(self, compute_data=True): + '''Compute the contact forces from q, v and elastic model''' + if compute_data: + pin.forwardKinematics(self.model, self.data, self.q, self.v) + # Computes the placements of all the operational frames according to the current joint placement stored in data + pin.updateFramePlacements(self.model, self.data) + self.collision_detection() + + i = 0 + for c in self.contacts: + self.f[i:i+3] = c.compute_force() + self.Jc[i:i+3, :] = c.get_jacobian() + i += 3 + return self.f + + def step(self, u, dt=None): + if dt is None: + dt = self.dt + + # (Forces are directly in the world frame, and aba wants them in the end effector frame) + pin.computeAllTerms(self.model, self.data, self.q, self.v) + pin.updateFramePlacements(self.model, self.data) + M = self.data.M + h = self.data.nle + self.collision_detection() + self.compute_forces(False) + + if(self.simulate_coulomb_friction and self.simulation_type=='timestepping'): + # minimize kinetic energy using time stepping + from quadprog import solve_qp + ''' + Solve a strictly convex quadratic program + + Minimize 1/2 x^T G x - a^T x + Subject to C.T x >= b + + Input Parameters + ---------- + G : array, shape=(n, n) + a : array, shape=(n,) + C : array, shape=(n, m) matrix defining the constraints + b : array, shape=(m), default=None, vector defining the constraints + meq : int, default=0 + the first meq constraints are treated as equality constraints, + all further as inequality constraints + ''' + # M (v' - v) = dt*S^T*(tau - tau_c) - dt*h + dt*J^T*f + # M v' = M*v + dt*(S^T*tau - h + J^T*f) - dt*S^T*tau_c + # M v' = b + B*tau_c + # v' = Minv*(b + B*tau_c) + b = M.dot(self.v) + dt*(self.S.T.dot(u) - h + self.Jc.T.dot(self.f)) + B = - dt*self.S.T + # Minimize kinetic energy: + # min v'.T * M * v' + # min (b+B*tau_c​).T*Minv*(b+B*tau_c​) + # min tau_c.T * B.T*Minv*B* tau_C + 2*b.T*Minv*B*tau_c + Minv = np.linalg.inv(M) + G = B.T.dot(Minv.dot(B)) + a = -b.T.dot(Minv.dot(B)) + C = np.vstack((np.eye(self.na), -np.eye(self.na))) + c = np.concatenate((-self.tau_coulomb_max, -self.tau_coulomb_max)) + solution = solve_qp(G, a, C.T, c, 0) + self.tau_c = solution[0] + self.v = Minv.dot(b + B.dot(self.tau_c)) + self.q = pin.integrate(self.model, self.q, self.v*dt) + self.dv = np.linalg.solve(M, self.S.T.dot(u-self.tau_c) - h + self.Jc.T.dot(self.f)) + elif(self.simulation_type=='euler' or self.simulate_coulomb_friction==False): + self.tau_c = self.tau_coulomb_max*np.sign(self.v[-self.na:]) + self.dv = np.linalg.solve(M, self.S.T.dot(u-self.tau_c) - h + self.Jc.T.dot(self.f)) + # v_mean = np.copy(self.v) + 0.5*dt*self.dv + self.q = pin.integrate(self.model, self.q, self.v*dt) + # self.q += dt*v_mean + self.v += self.dv*dt + else: + print("[ERROR] Unknown simulation type:", self.simulation_type) + + self.t += dt + return self.q, self.v, self.dv + + def reset(self): + self.first_iter = True + + def simulate(self, state, u, dt=0.05, ndt=1): + ''' Perform ndt steps, each lasting dt/ndt seconds ''' + self.q = state[:self.robot.nq] + self.v = state[self.robot.nq:self.robot.nv+self.robot.nq] + + tau_c_avg = 0*self.tau_c + for i in range(ndt): + self.q, self.v, self.dv = self.step(u, dt/ndt) + tau_c_avg += self.tau_c + self.tau_c = tau_c_avg / ndt + + if (self.use_viewer): + self.display_counter -= dt + if self.display_counter <= 0.0: + self.robot.display(self.q) + self.display_counter = self.DISPLAY_T + + return self.q, self.v, self.f + + def display(self, q): + if(self.use_viewer): + self.robot.display(q) + + +class RobotWrapper(PinocchioRobotWrapper): + + @staticmethod + def BuildFromURDF(filename, package_dirs=None, root_joint=None, verbose=False, meshLoader=None): + robot = RobotWrapper() + robot.initFromURDF(filename, package_dirs, root_joint, verbose, meshLoader) + return robot + + @property + def na(self): + if(self.model.joints[0].nq==7): + return self.model.nv-6 + return self.model.nv + + def mass(self, q, update=True): + if(update): + return pin.crba(self.model, self.data, q) + return self.data.M + + def nle(self, q, v, update=True): + if(update): + return pin.nonLinearEffects(self.model, self.data, q, v) + return self.data.nle + + def com(self, q=None, v=None, a=None, update=True): + if(update==False or q is None): + return PinocchioRobotWrapper.com(self, q) + if a is None: + if v is None: + return PinocchioRobotWrapper.com(self, q) + return PinocchioRobotWrapper.com(self, q, v) + return PinocchioRobotWrapper.com(self, q, v,a) + + def Jcom(self, q, update=True): + if(update): + return pin.jacobianCenterOfMass(self.model, self.data, q) + return self.data.Jcom + + def momentumJacobian(self, q, v, update=True): + if(update): + pin.ccrba(self.model, self.data, q, v) + return self.data.Ag + + def computeAllTerms(self, q, v): + ''' pin.computeAllTerms is equivalent to calling: + pinocchio::forwardKinematics + pinocchio::crba + pinocchio::nonLinearEffects + pinocchio::computeJointJacobians + pinocchio::centerOfMass + pinocchio::jacobianCenterOfMass + pinocchio::kineticEnergy + pinocchio::potentialEnergy + This is too much for our needs, so we call only the functions + we need, including those for the frame kinematics + ''' + #pin.computeAllTerms(self.model, self.data, q, v); + pin.forwardKinematics(self.model, self.data, q, v, np.zeros(self.model.nv)) + pin.computeJointJacobians(self.model, self.data) + pin.updateFramePlacements(self.model, self.data) + pin.crba(self.model, self.data, q) + pin.nonLinearEffects(self.model, self.data, q, v) + + def forwardKinematics(self, q, v=None, a=None): + if v is not None: + if a is not None: + pin.forwardKinematics(self.model, self.data, q, v, a) + else: + pin.forwardKinematics(self.model, self.data, q, v) + else: + pin.forwardKinematics(self.model, self.data, q) + + def inverseKinematics(self, ee_coords): + ''' Inverse kinematics algorithm to compute a joint configuration given the EE coordinates''' + + oMdes = pin.SE3(np.eye(3), np.array([ee_coords[0], ee_coords[1], 0.0])) + random.seed(123) + for j in range(100): + q = np.array([random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi)]) + # q = pin.neutral(self.model) + + pin.forwardKinematics(self.model, self.data, q) + pin.updateFramePlacements(self.model, self.data) + eps = 1e-4 + IT_MAX = 1000 + DT = 1e-1 + damp = 1e-12 + frame_id = self.model.getFrameId('fixed_ee') + i=0 + while True: + pin.forwardKinematics(self.model,self.data,q) + pin.updateFramePlacements(self.model, self.data) + dMi = oMdes.actInv(self.data.oMf[frame_id]) + err = pin.log(dMi).vector + if norm(err) < eps: + success = True + break + if i >= IT_MAX: + success = False + break + J = pin.computeFrameJacobian(self.model,self.data,q,frame_id) + v = - J.T.dot(np.linalg.solve(J.dot(J.T) + damp * np.eye(6), err)) + q = pin.integrate(self.model,q,v*DT) + if not i % 10: + print('%d: error = %s' % (i, err.T)) + i += 1 + + if success: + print("Convergence achieved!") + break + else: + print("\nWarning: the iterative algorithm has not reached convergence to the desired precision. Retry ({}) with another initial configuration".format(j)) + + return q, success + + def frameJacobian(self, q, index, update=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + ''' Call computeFrameJacobian if update is true. If not, user should call computeFrameJacobian first. + Then call getFrameJacobian and return the Jacobian matrix. + ref_frame can be: ReferenceFrame.LOCAL, ReferenceFrame.WORLD, ReferenceFrame.LOCAL_WORLD_ALIGNED + ''' + if(update): + pin.computeFrameJacobian(self.model, self.data, q, index) + return pin.getFrameJacobian(self.model, self.data, index, ref_frame) + + def frameVelocity(self, q, v, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v) + v_local = pin.getFrameVelocity(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return v_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + v_world = H.act(v_local) + return v_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + v = Hr.act(v_local) + return v + + def frameAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def frameClassicAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + v = pin.getFrameVelocity(self.model, self.data, index) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + a_local.linear += np.cross(v.angular, v.linear, axis=0) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def deactivateCollisionPairs(self, collision_pair_indexes): + for i in collision_pair_indexes: + self.collision_data.deactivateCollisionPair(i) + + def addAllCollisionPairs(self): + self.collision_model.addAllCollisionPairs() + self.collision_data = pin.GeometryData(self.collision_model) + + def isInCollision(self, q, stop_at_first_collision=True): + return pin.computeCollisions(self.model, self.data, self.collision_model, self.collision_data, np.asmatrix(q).reshape((self.model.nq,1)), stop_at_first_collision) + + def findFirstCollisionPair(self, consider_only_active_collision_pairs=True): + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + return (i, self.collision_model.collisionPairs[i]) + return None + + def findAllCollisionPairs(self, consider_only_active_collision_pairs=True): + res = [] + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + res += [(i, self.collision_model.collisionPairs[i])] + return res \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Code/N_try_6/segment_tree.py b/Results Double Integrator/Results set test/Code/N_try_6/segment_tree.py new file mode 100644 index 0000000..cb386ec --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/segment_tree.py @@ -0,0 +1,145 @@ +import operator + + +class SegmentTree(object): + def __init__(self, capacity, operation, neutral_element): + """Build a Segment Tree data structure. + + https://en.wikipedia.org/wiki/Segment_tree + + Can be used as regular array, but with two + important differences: + + a) setting item's value is slightly slower. + It is O(lg capacity) instead of O(1). + b) user has access to an efficient ( O(log segment size) ) + `reduce` operation which reduces `operation` over + a contiguous subsequence of items in the array. + + Paramters + --------- + capacity: int + Total size of the array - must be a power of two. + operation: lambda obj, obj -> obj + and operation for combining elements (eg. sum, max) + must form a mathematical group together with the set of + possible values for array elements (i.e. be associative) + neutral_element: obj + neutral element for the operation above. eg. float('-inf') + for max and 0 for sum. + """ + assert capacity > 0 and capacity & (capacity - 1) == 0, "capacity must be positive and a power of 2." + self._capacity = capacity + self._value = [neutral_element for _ in range(2 * capacity)] + self._operation = operation + + def _reduce_helper(self, start, end, node, node_start, node_end): + if start == node_start and end == node_end: + return self._value[node] + mid = (node_start + node_end) // 2 + if end <= mid: + return self._reduce_helper(start, end, 2 * node, node_start, mid) + else: + if mid + 1 <= start: + return self._reduce_helper(start, end, 2 * node + 1, mid + 1, node_end) + else: + return self._operation( + self._reduce_helper(start, mid, 2 * node, node_start, mid), + self._reduce_helper(mid + 1, end, 2 * node + 1, mid + 1, node_end) + ) + + def reduce(self, start=0, end=None): + """Returns result of applying `self.operation` + to a contiguous subsequence of the array. + + self.operation(arr[start], operation(arr[start+1], operation(... arr[end]))) + + Parameters + ---------- + start: int + beginning of the subsequence + end: int + end of the subsequences + + Returns + ------- + reduced: obj + result of reducing self.operation over the specified range of array elements. + """ + if end is None: + end = self._capacity + if end < 0: + end += self._capacity + end -= 1 + return self._reduce_helper(start, end, 1, 0, self._capacity - 1) + + def __setitem__(self, idx, val): + # index of the leaf + idx += self._capacity + self._value[idx] = val + idx //= 2 + while idx >= 1: + self._value[idx] = self._operation( + self._value[2 * idx], + self._value[2 * idx + 1] + ) + idx //= 2 + + def __getitem__(self, idx): + assert 0 <= idx < self._capacity + return self._value[self._capacity + idx] + + +class SumSegmentTree(SegmentTree): + def __init__(self, capacity): + super(SumSegmentTree, self).__init__( + capacity=capacity, + operation=operator.add, + neutral_element=0.0 + ) + + def sum(self, start=0, end=None): + """Returns arr[start] + ... + arr[end]""" + return super(SumSegmentTree, self).reduce(start, end) + + def find_prefixsum_idx(self, prefixsum): + """Find the highest index `i` in the array such that + sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum + + if array values are probabilities, this function + allows to sample indexes according to the discrete + probability efficiently. + + Parameters + ---------- + perfixsum: float + upperbound on the sum of array prefix + + Returns + ------- + idx: int + highest index satisfying the prefixsum constraint + """ + assert 0 <= prefixsum <= self.sum() + 1e-5 + idx = 1 + while idx < self._capacity: # while non-leaf + if self._value[2 * idx] > prefixsum: + idx = 2 * idx + else: + prefixsum -= self._value[2 * idx] + idx = 2 * idx + 1 + return idx - self._capacity + + +class MinSegmentTree(SegmentTree): + def __init__(self, capacity): + super(MinSegmentTree, self).__init__( + capacity=capacity, + operation=min, + neutral_element=float('inf') + ) + + def min(self, start=0, end=None): + """Returns min(arr[start], ..., arr[end])""" + + return super(MinSegmentTree, self).reduce(start, end) diff --git a/Results Double Integrator/Results set test/Code/N_try_6/utils.py b/Results Double Integrator/Results set test/Code/N_try_6/utils.py new file mode 100644 index 0000000..c426b89 --- /dev/null +++ b/Results Double Integrator/Results set test/Code/N_try_6/utils.py @@ -0,0 +1,42 @@ +import tensorflow as tf +import numpy as np + +def array2tensor(array): + + return tf.expand_dims(tf.convert_to_tensor(array), 0) + +def de_normalize_tensor(state, state_norm_arr): + ''' Retrieve state from normalized state - tensor ''' + state_time = tf.concat([tf.zeros([state.shape[0], state.shape[1]-1]), tf.reshape((state[:,-1]+1)*state_norm_arr[-1]/2,[state.shape[0],1])],1) + state_no_time = state * state_norm_arr + mask = tf.concat([tf.ones([state.shape[0], state.shape[1]-1]), tf.zeros([state.shape[0], 1])],1) + state_not_norm = state_no_time * mask + state_time * (1 - mask) + + return state_not_norm + +def normalize_tensor(state, state_norm_arr): + ''' Retrieve state from normalized state - tensor ''' + state_norm_time = tf.concat([tf.zeros([state.shape[0], state.shape[1]-1]), tf.reshape(((state[:,-1]) / state_norm_arr[-1])*2 - 1,[state.shape[0],1])],1) + state_norm_no_time = state / state_norm_arr + mask = tf.concat([tf.ones([state.shape[0], state.shape[1]-1]), tf.zeros([state.shape[0], 1])],1) + state_norm = state_norm_no_time * mask + state_norm_time * (1 - mask) + + return state_norm + +def de_normalize(state, state_norm_arr): + ''' Retrieve state from normalized state ''' + state_not_norm = np.empty_like(state) + state_not_norm[:-1] = state[:-1] * state_norm_arr[:-1] + state_not_norm[-1] = (state[-1] + 1) * state_norm_arr[-1]/2 + + return state_not_norm + +def normalize(state, state_norm_arr): + ''' Normalize state ''' + state_norm = np.empty_like(state) + state_norm = state / state_norm_arr + state_norm[-1] = state_norm[-1] * 2 -1 + + return state_norm + + diff --git a/Results Double Integrator/Results set test/Configs/conf_double_integrator_0.py b/Results Double Integrator/Results set test/Configs/conf_double_integrator_0.py new file mode 100644 index 0000000..2c9dc77 --- /dev/null +++ b/Results Double Integrator/Results set test/Configs/conf_double_integrator_0.py @@ -0,0 +1,215 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'double_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 50000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 18000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 200 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-4 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 1 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Double Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Double Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Double Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Double Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Double Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Double Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Double Integrator/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Robot upload data +URDF_FILENAME = "double_integrator.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + +q_init, v_init = np.array([-5, 0]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max) + +### State parameters +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, dt]) # State lower bound vector +x_init_min = np.array([-15, -15, -6, -6, dt]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, 6, 6, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, 6, 6, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([12.0, 2.0, 0.0, 0.0, 0.0]), + np.array([12.0, -2.0, 0.0, 0.0, 0.0]), + np.array([15.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = robot.na # Action size +tau_lower_bound = -2 # Action lower bound +tau_upper_bound = 2 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-15, 15], [-15, 15]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag + +# {'test_n': 0, 'seed': 0, 'system_id': 'double_integrator', 'recover_training_flag': False, 'nb_cpus': 15, 'w_S': 0.01} \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Configs/conf_double_integrator_6.py b/Results Double Integrator/Results set test/Configs/conf_double_integrator_6.py new file mode 100644 index 0000000..a5ff55a --- /dev/null +++ b/Results Double Integrator/Results set test/Configs/conf_double_integrator_6.py @@ -0,0 +1,215 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'double_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 50000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 18000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 200 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-4 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 1 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Double Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Double Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Double Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Double Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Double Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Double Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Double Integrator/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Robot upload data +URDF_FILENAME = "double_integrator.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + +q_init, v_init = np.array([-5, 0]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max) + +### State parameters +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, dt]) # State lower bound vector +x_init_min = np.array([-15, -15, -6, -6, dt]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, 6, 6, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, 6, 6, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([12.0, 2.0, 0.0, 0.0, 0.0]), + np.array([12.0, -2.0, 0.0, 0.0, 0.0]), + np.array([15.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = robot.na # Action size +tau_lower_bound = -2 # Action lower bound +tau_upper_bound = 2 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-15, 15], [-15, 15]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag + +# {'test_n': 6, 'seed': 0, 'system_id': 'double_integrator', 'recover_training_flag': False, 'nb_cpus': 15, 'w_S': 0.01} \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Configs/config0.txt b/Results Double Integrator/Results set test/Configs/config0.txt new file mode 100644 index 0000000..082802c --- /dev/null +++ b/Results Double Integrator/Results set test/Configs/config0.txt @@ -0,0 +1,130 @@ +os = +math = +np = +cpin = +RobotWrapper = +RobotSimulator = +system_id = double_integrator +EP_UPDATE = 200 +NUPDATES = 50000 +UPDATE_LOOPS = [ 1000 4000 7000 10000 13000 16000] +NEPISODES = 1200 +NLOOPS = 6 +NSTEPS = 200 +CRITIC_LEARNING_RATE = 0.0005 +ACTOR_LEARNING_RATE = 0.001 +REPLAY_SIZE = 65536 +BATCH_SIZE = 128 +MC = 0 +UPDATE_RATE = 0.001 +nsteps_TD_N = 50 +save_flag = 1 +save_interval = 5000 +plot_flag = 1 +plot_rollout_interval = 400 +plot_rollout_interval_diff_loc = 6000 +critic_type = sine +NH1 = 256 +NH2 = 256 +LR_SCHEDULE = 0 +boundaries_schedule_LR_C = [102400.0, 153600.0, 204800.0, 256000.0] +values_schedule_LR_C = [0.0005, 0.00025, 0.000125, 6.25e-05, 3.125e-05] +boundaries_schedule_LR_A = [102400.0, 153600.0, 204800.0, 256000.0] +values_schedule_LR_A = [0.001, 0.0005, 0.00025, 0.000125, 6.25e-05] +NORMALIZE_INPUTS = 1 +kreg_l1_A = 0.01 +kreg_l2_A = 0.01 +breg_l1_A = 0.01 +breg_l2_A = 0.01 +kreg_l1_C = 0.01 +kreg_l2_C = 0.01 +breg_l1_C = 0.01 +breg_l2_C = 0.01 +prioritized_replay_alpha = 0 +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None +prioritized_replay_eps = 0.0001 +fresh_factor = 1 +XC1 = -2.0 +YC1 = 0.0 +A1 = 6 +B1 = 10 +XC2 = 3.0 +YC2 = 4.0 +A2 = 12 +B2 = 4 +XC3 = 3.0 +YC3 = -4.0 +A3 = 12 +B3 = 4 +obs_param = [-2. 0. 3. 4. 3. -4. 6. 10. 12. 4. 12. 4.] +w_d = 100 +w_u = 10 +w_peak = 500000.0 +w_ob = 5000000.0 +w_v = 0 +weight = [1.e+02 1.e+01 5.e+05 5.e+06 0.e+00] +cost_weights_running = [1.e+02 5.e+05 0.e+00 5.e+06 5.e+06 5.e+06 1.e+01] +cost_weights_terminal = [1.e+02 5.e+05 0.e+00 5.e+06 5.e+06 5.e+06 0.e+00] +alpha = 50 +alpha2 = 5 +soft_max_param = [50 5] +offset_cost_fun = 0 +scale_cost_fun = 1e-05 +cost_funct_param = [0.e+00 1.e-05] +x_des = -7.0 +y_des = 0.0 +TARGET_STATE = [-7. 0.] +test_set = set test +Config_path = ./Results Double Integrator/Results set test/Configs/ +Fig_path = ./Results Double Integrator/Results set test/Figures +NNs_path = ./Results Double Integrator/Results set test/NNs +Log_path = ./Results Double Integrator/Results set test/Log/ +Code_path = ./Results Double Integrator/Results set test/Code/ +DictWS_path = ./Results Double Integrator/Results set test/DictWS/ +path_list = ['./Results Double Integrator/Results set test/Figures', './Results Double Integrator/Results set test/NNs', './Results Double Integrator/Results set test/Log/', './Results Double Integrator/Results set test/Code/', './Results Double Integrator/Results set test/DictWS/'] +test_set_rec = None +NNs_path_rec = ./Results Double Integrator/Results set None/NNs +N_try_rec = None +update_step_counter_rec = None +env_RL = 0 +URDF_FILENAME = double_integrator.urdf +modelPath = /home/a2rlab2/cacto/urdf/double_integrator.urdf +robot = +nq = 2 +nv = 2 +nx = 4 +na = 2 +cmodel = Nb joints = 3 (nq=2,nv=2) + Joint 0 universe: parent=0 + Joint 1 slider_x: parent=0 + Joint 2 slider_y: parent=1 + +cdata = +end_effector_frame_id = EE +dt = 0.05 +simulate_coulomb_friction = 0 +simulation_type = euler +tau_coulomb_max = [0. 0.] +integration_scheme = E-Euler +q_init = [-5 0] +v_init = [0. 0.] +simu = +nb_state = 5 +x_min = [-inf -inf -inf -inf 0.05] +x_init_min = [-15. -15. -6. -6. 0.05] +x_max = [inf inf inf inf inf] +x_init_max = [15. 15. 6. 6. 9.95] +state_norm_arr = [15 15 6 6 10] +init_states_sim = [array([2., 0., 0., 0., 0.]), array([10., 0., 0., 0., 0.]), array([ 10., -10., 0., 0., 0.]), array([10., 10., 0., 0., 0.]), array([-10., 10., 0., 0., 0.]), array([-10., -10., 0., 0., 0.]), array([12., 2., 0., 0., 0.]), array([12., -2., 0., 0., 0.]), array([15., 0., 0., 0., 0.])] +nb_action = 2 +tau_lower_bound = -2 +tau_upper_bound = 2 +u_min = [-2. -2.] +u_max = [2. 2.] +w_b = 0.1 +fig_ax_lim = [[-15 15] + [-15 15]] +profile = 0 +Seed = 0 +w_S = 0.01 \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Configs/config6.txt b/Results Double Integrator/Results set test/Configs/config6.txt new file mode 100644 index 0000000..76f9782 --- /dev/null +++ b/Results Double Integrator/Results set test/Configs/config6.txt @@ -0,0 +1,130 @@ +os = +math = +np = +cpin = +RobotWrapper = +RobotSimulator = +system_id = double_integrator +EP_UPDATE = 200 +NUPDATES = 50000 +UPDATE_LOOPS = [ 1000 4000 7000 10000 13000 16000] +NEPISODES = 1200 +NLOOPS = 6 +NSTEPS = 200 +CRITIC_LEARNING_RATE = 0.0005 +ACTOR_LEARNING_RATE = 0.001 +REPLAY_SIZE = 65536 +BATCH_SIZE = 128 +MC = 0 +UPDATE_RATE = 0.001 +nsteps_TD_N = 50 +save_flag = 1 +save_interval = 5000 +plot_flag = 1 +plot_rollout_interval = 400 +plot_rollout_interval_diff_loc = 6000 +critic_type = sine +NH1 = 256 +NH2 = 256 +LR_SCHEDULE = 0 +boundaries_schedule_LR_C = [102400.0, 153600.0, 204800.0, 256000.0] +values_schedule_LR_C = [0.0005, 0.00025, 0.000125, 6.25e-05, 3.125e-05] +boundaries_schedule_LR_A = [102400.0, 153600.0, 204800.0, 256000.0] +values_schedule_LR_A = [0.001, 0.0005, 0.00025, 0.000125, 6.25e-05] +NORMALIZE_INPUTS = 1 +kreg_l1_A = 0.01 +kreg_l2_A = 0.01 +breg_l1_A = 0.01 +breg_l2_A = 0.01 +kreg_l1_C = 0.01 +kreg_l2_C = 0.01 +breg_l1_C = 0.01 +breg_l2_C = 0.01 +prioritized_replay_alpha = 0 +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None +prioritized_replay_eps = 0.0001 +fresh_factor = 1 +XC1 = -2.0 +YC1 = 0.0 +A1 = 6 +B1 = 10 +XC2 = 3.0 +YC2 = 4.0 +A2 = 12 +B2 = 4 +XC3 = 3.0 +YC3 = -4.0 +A3 = 12 +B3 = 4 +obs_param = [-2. 0. 3. 4. 3. -4. 6. 10. 12. 4. 12. 4.] +w_d = 100 +w_u = 10 +w_peak = 500000.0 +w_ob = 5000000.0 +w_v = 0 +weight = [1.e+02 1.e+01 5.e+05 5.e+06 0.e+00] +cost_weights_running = [1.e+02 5.e+05 0.e+00 5.e+06 5.e+06 5.e+06 1.e+01] +cost_weights_terminal = [1.e+02 5.e+05 0.e+00 5.e+06 5.e+06 5.e+06 0.e+00] +alpha = 50 +alpha2 = 5 +soft_max_param = [50 5] +offset_cost_fun = 0 +scale_cost_fun = 1e-05 +cost_funct_param = [0.e+00 1.e-05] +x_des = -7.0 +y_des = 0.0 +TARGET_STATE = [-7. 0.] +test_set = set test +Config_path = ./Results Double Integrator/Results set test/Configs/ +Fig_path = ./Results Double Integrator/Results set test/Figures +NNs_path = ./Results Double Integrator/Results set test/NNs +Log_path = ./Results Double Integrator/Results set test/Log/ +Code_path = ./Results Double Integrator/Results set test/Code/ +DictWS_path = ./Results Double Integrator/Results set test/DictWS/ +path_list = ['./Results Double Integrator/Results set test/Figures', './Results Double Integrator/Results set test/NNs', './Results Double Integrator/Results set test/Log/', './Results Double Integrator/Results set test/Code/', './Results Double Integrator/Results set test/DictWS/'] +test_set_rec = None +NNs_path_rec = ./Results Double Integrator/Results set None/NNs +N_try_rec = None +update_step_counter_rec = None +env_RL = 0 +URDF_FILENAME = double_integrator.urdf +modelPath = /home/a2rlab2/cacto/urdf/double_integrator.urdf +robot = +nq = 2 +nv = 2 +nx = 4 +na = 2 +cmodel = Nb joints = 3 (nq=2,nv=2) + Joint 0 universe: parent=0 + Joint 1 slider_x: parent=0 + Joint 2 slider_y: parent=1 + +cdata = +end_effector_frame_id = EE +dt = 0.05 +simulate_coulomb_friction = 0 +simulation_type = euler +tau_coulomb_max = [0. 0.] +integration_scheme = E-Euler +q_init = [-5 0] +v_init = [0. 0.] +simu = +nb_state = 5 +x_min = [-inf -inf -inf -inf 0.05] +x_init_min = [-15. -15. -6. -6. 0.05] +x_max = [inf inf inf inf inf] +x_init_max = [15. 15. 6. 6. 9.95] +state_norm_arr = [15 15 6 6 10] +init_states_sim = [array([2., 0., 0., 0., 0.]), array([10., 0., 0., 0., 0.]), array([ 10., -10., 0., 0., 0.]), array([10., 10., 0., 0., 0.]), array([-10., 10., 0., 0., 0.]), array([-10., -10., 0., 0., 0.]), array([12., 2., 0., 0., 0.]), array([12., -2., 0., 0., 0.]), array([15., 0., 0., 0., 0.])] +nb_action = 2 +tau_lower_bound = -2 +tau_upper_bound = 2 +u_min = [-2. -2.] +u_max = [2. 2.] +w_b = 0.1 +fig_ax_lim = [[-15 15] + [-15 15]] +profile = 0 +Seed = 0 +w_S = 0.01 \ No newline at end of file diff --git a/Results Double Integrator/Results set test/Figures/N_try_6/EpReturn_6.png b/Results Double Integrator/Results set test/Figures/N_try_6/EpReturn_6.png new file mode 100644 index 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(int) 1st hidden layer size + :param NH2: (int) 2nd hidden layer size + :param kreg_l1_A : (float) Weight of L1 regularization in actor's network - kernel + :param kreg_l2_A : (float) Weight of L2 regularization in actor's network - kernel + :param breg_l1_A : (float) Weight of L2 regularization in actor's network - bias + :param breg_l2_A : (float) Weight of L2 regularization in actor's network - bias + :param kreg_l1_C : (float) Weight of L1 regularization in critic's network - kernel + :param kreg_l2_C : (float) Weight of L2 regularization in critic's network - kernel + :param breg_l1_C : (float) Weight of L1 regularization in critic's network - bias + :param breg_l2_C : (float) Weight of L2 regularization in critic's network - bias + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param NORMALIZE_INPUTS : (bool) Flag to normalize inputs (state) + :param state_norm_array : (float array) Array used to normalize states + :param MC : (bool) Flag to use MC or TD(n) + :param cost_weights_terminal : (float array) Running cost weights vector + :param cost_weights_running : (float array) Terminal cost weights vector + :param BATCH_SIZE : (int) Size of the mini-batch + :param dt : (float) Timestep + + :input w_S : (float) Sobolev-training weight + ''' + + self.env = env + self.conf = conf + + self.w_S = w_S + + self.MSE = tf.keras.losses.MeanSquaredError() + + return + + def create_actor(self): + ''' Create actor NN ''' + inputs = layers.Input(shape=(self.conf.nb_state,)) + + lay1 = layers.Dense(self.conf.NH1,kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(inputs) + leakyrelu1 = layers.LeakyReLU()(lay1) + lay2 = layers.Dense(self.conf.NH2, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(leakyrelu1) + leakyrelu2 = layers.LeakyReLU()(lay2) + outputs = layers.Dense(self.conf.nb_action, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(leakyrelu2) + + model = tf.keras.Model(inputs, outputs) + + return model + + def create_critic_elu(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = layers.Dense(16, activation='elu')(state_input) + state_out2 = layers.Dense(32, activation='elu')(state_out1) + out_lay1 = layers.Dense(256, activation='elu')(state_out2) + out_lay2 = layers.Dense(256, activation='elu')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_sine_elu(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = SinusodialRepresentationDense(64, activation='sine')(state_input) + state_out2 = layers.Dense(64, activation='elu')(state_out1) + out_lay1 = SinusodialRepresentationDense(128, activation='sine')(state_out2) + out_lay2 = layers.Dense(128, activation='elu')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_sine(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = SinusodialRepresentationDense(64, activation='sine')(state_input) + state_out2 = SinusodialRepresentationDense(64, activation='sine')(state_out1) + out_lay1 = SinusodialRepresentationDense(128, activation='sine')(state_out2) + out_lay2 = SinusodialRepresentationDense(128, activation='sine')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_relu(self): + ''' Create critic NN - relu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = layers.Dense(16, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(state_input) + leakyrelu1 = layers.LeakyReLU()(state_out1) + + state_out2 = layers.Dense(32, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu1) + leakyrelu2 = layers.LeakyReLU()(state_out2) + out_lay1 = layers.Dense(self.conf.NH1, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu2) + leakyrelu3 = layers.LeakyReLU()(out_lay1) + out_lay2 = layers.Dense(self.conf.NH2, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu3) + leakyrelu4 = layers.LeakyReLU()(out_lay2) + + outputs = layers.Dense(1, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu4) + + model = tf.keras.Model([state_input], outputs) + + return model + + def eval(self, NN, input): + ''' Compute the output of a NN given an input ''' + if not tf.is_tensor(input): + input = tf.convert_to_tensor(input, dtype=tf.float32) + + if self.conf.NORMALIZE_INPUTS: + input = normalize_tensor(input, self.conf.state_norm_arr) + + return NN(input, training=True) + + def custom_logarithm(self,input): + # Calculate the logarithms based on the non-zero condition + positive_log = tf.math.log(tf.math.maximum(input, 1e-7) + 1) + negative_log = -tf.math.log(tf.math.maximum(-input, 1e-7) + 1) + + # Use the appropriate logarithm based on the condition + result = tf.where(input > 0, positive_log, negative_log) + + return result + + def compute_critic_grad(self, critic_model, target_critic, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, weights_batch): + ''' Compute the gradient of the critic NN ''' + with tf.GradientTape() as tape: + # Compute value function tail if TD(n) is used + if self.conf.MC: + reward_to_go_batch = partial_reward_to_go_batch + else: + target_values = self.eval(target_critic, state_next_rollout_batch) # Compute Value at next state after conf.nsteps_TD_N steps given by target critic + reward_to_go_batch = partial_reward_to_go_batch + (1-d_batch)*target_values # Compute batch of 1-step targets for the critic loss + + # Compute critic loss + if self.w_S != 0: + with tf.GradientTape() as tape2: + tape2.watch(state_batch) + critic_value = self.eval(critic_model, state_batch) + der_critic_value = tape2.gradient(critic_value, state_batch) + + critic_loss_v = self.MSE(reward_to_go_batch, critic_value, sample_weight=weights_batch) + critic_loss_der = self.MSE(self.custom_logarithm(dVdx_batch[:,:-1]), self.custom_logarithm(der_critic_value[:,:-1]), sample_weight=weights_batch) # dV/dt not computed and so not used in the update + + critic_loss = critic_loss_der + self.w_S*critic_loss_v + else: + critic_value = self.eval(critic_model, state_batch) + critic_loss = self.MSE(reward_to_go_batch, critic_value, sample_weight=weights_batch) + + # Compute the gradients of the critic loss w.r.t. critic's parameters + critic_grad = tape.gradient(critic_loss, critic_model.trainable_variables) + + return critic_grad, reward_to_go_batch, critic_value, self.eval(target_critic, state_batch) + + def compute_actor_grad(self, actor_model, critic_model, state_batch, term_batch, batch_size): + ''' Compute the gradient of the actor NN ''' + if batch_size == None: + batch_size = self.conf.BATCH_SIZE + + actions = self.eval(actor_model, state_batch) + + # Both take into account normalization, ds_next_da is the gradient of the dynamics w.r.t. policy actions (ds'_da) + state_next_tf, ds_next_da = self.env.simulate_batch(state_batch.numpy(), actions.numpy()) , self.env.derivative_batch(state_batch.numpy(), actions.numpy()) + + with tf.GradientTape() as tape: + tape.watch(state_next_tf) + critic_value_next = self.eval(critic_model,state_next_tf) + + # dV_ds' = gradient of V w.r.t. s', where s'=f(s,a) a=policy(s) + dV_ds_next = tape.gradient(critic_value_next, state_next_tf) + + cost_weights_terminal_reshaped = np.reshape(self.conf.cost_weights_terminal,[1,len(self.conf.cost_weights_terminal)]) + cost_weights_running_reshaped = np.reshape(self.conf.cost_weights_running,[1,len(self.conf.cost_weights_running)]) + with tf.GradientTape() as tape1: + tape1.watch(actions) + rewards_tf = self.env.reward_batch(term_batch.dot(cost_weights_terminal_reshaped) + (1-term_batch).dot(cost_weights_running_reshaped), state_batch.numpy(), actions) + + # dr_da = gradient of reward r(s,a) w.r.t. policy's action a + dr_da = tape1.gradient(rewards_tf, actions, unconnected_gradients=tf.UnconnectedGradients.ZERO) + + dr_da_reshaped = tf.reshape(dr_da, (batch_size, 1, self.conf.nb_action)) + + # dr_ds' + dV_ds' (note: dr_ds' = 0) + dQ_ds_next = tf.reshape(dV_ds_next, (batch_size, 1, self.conf.nb_state)) + + # (dr_ds' + dV_ds')*ds'_da + dQ_ds_next_da = tf.matmul(dQ_ds_next, ds_next_da) + + # (dr_ds' + dV_ds')*ds'_da + dr_da + dQ_da = dQ_ds_next_da + dr_da_reshaped + + # Now let's multiply -[(dr_ds' + dV_ds')*ds'_da + dr_da] by the actions a + # and then let's autodifferentiate w.r.t theta_A (actor NN's parameters) to finally get -dQ/dtheta_A + with tf.GradientTape() as tape: + tape.watch(actor_model.trainable_variables) + actions = self.eval(actor_model, state_batch) + + actions_reshaped = tf.reshape(actions,(batch_size,self.conf.nb_action,1)) + dQ_da_reshaped = tf.reshape(dQ_da,(batch_size,1,self.conf.nb_action)) + Q_neg = tf.matmul(-dQ_da_reshaped,actions_reshaped) + + # Also here we need a scalar so we compute the mean -Q across the batch + mean_Qneg = tf.math.reduce_mean(Q_neg) + + # Gradients of the actor loss w.r.t. actor's parameters + actor_grad = tape.gradient(mean_Qneg, actor_model.trainable_variables) + + return actor_grad \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_0/RL.py b/Results Single Integrator/Results set test/Code/N_try_0/RL.py new file mode 100644 index 0000000..fa60798 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/RL.py @@ -0,0 +1,233 @@ +import uuid +import math +import numpy as np +import tensorflow as tf + +class RL_AC: + def __init__(self, env, NN, conf, N_try): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :parma critic_type : (str) Activation function to use for the critic NN + :param LR_SCHEDULE : (bool) Flag to use a scheduler for the learning rates + :param boundaries_schedule_LR_C : (list) Boudaries of critic LR + :param values_schedule_LR_C : (list) Values of critic LR + :param boundaries_schedule_LR_A : (list) Boudaries of actor LR + :param values_schedule_LR_A : (list) Values of actor LR + :param CRITIC_LEARNING_RATE : (float) Learning rate for the critic network + :param ACTOR_LEARNING_RATE : (float) Learning rate for the policy network + :param fresh_factor : (float) Refresh factor + :param prioritized_replay_alpha : (float) α determines how much prioritization is used + :param prioritized_replay_eps : (float) It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero + :param UPDATE_LOOPS : (int array) Number of updates of both critic and actor performed every EP_UPDATE episodes + :param save_interval : (int) save NNs interval + :param env_RL : (bool) Flag RL environment + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param MC : (bool) Flag to use MC or TD(n) + :param nsteps_TD_N : (int) Number of lookahed steps if TD(n) is used + :param UPDATE_RATE : (float) Homotopy rate to update the target critic network if TD(n) is used + :param cost_weights_terminal : (float array) Running cost weights vector + :param cost_weights_running : (float array) Terminal cost weights vector + :param dt : (float) Timestep + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param NNs_path : (str) NNs path + :param NSTEPS : (int) Max episode length + + ''' + self.env = env + self.NN = NN + self.conf = conf + + self.N_try = N_try + + self.actor_model = None + self.critic_model = None + self.target_critic = None + self.actor_optimizer = None + self.critic_optimizer = None + + self.init_rand_state = None + self.NSTEPS_SH = 0 + self.control_arr = None + self.state_arr = None + self.ee_pos_arr = None + self.exp_counter = np.zeros(conf.REPLAY_SIZE) + + return + + def setup_model(self, recover_training=None): + ''' Setup RL model ''' + # Create actor, critic and target NNs + self.actor_model = self.NN.create_actor() + + if self.conf.critic_type == 'elu': + self.critic_model = self.NN.create_critic_elu() + self.target_critic = self.NN.create_critic_elu() + elif self.conf.critic_type == 'sine': + self.critic_model = self.NN.create_critic_sine() + self.target_critic = self.NN.create_critic_sine() + elif self.conf.critic_type == 'sine-elu': + self.critic_model = self.NN.create_critic_sine_elu() + self.target_critic = self.NN.create_critic_sine_elu() + else: + self.critic_model = self.NN.create_critic_relu() + self.target_critic = self.NN.create_critic_relu() + + # Set optimizer specifying the learning rates + if self.conf.LR_SCHEDULE: + # Piecewise constant decay schedule + self.CRITIC_LR_SCHEDULE = tf.keras.optimizers.schedules.PiecewiseConstantDecay(self.conf.boundaries_schedule_LR_C, self.conf.values_schedule_LR_C) + self.ACTOR_LR_SCHEDULE = tf.keras.optimizers.schedules.PiecewiseConstantDecay(self.conf.boundaries_schedule_LR_A, self.conf.values_schedule_LR_A) + self.critic_optimizer = tf.keras.optimizers.Adam(self.CRITIC_LR_SCHEDULE) + self.actor_optimizer = tf.keras.optimizers.Adam(self.ACTOR_LR_SCHEDULE) + else: + self.critic_optimizer = tf.keras.optimizers.Adam(self.conf.CRITIC_LEARNING_RATE) + self.actor_optimizer = tf.keras.optimizers.Adam(self.conf.ACTOR_LEARNING_RATE) + + # Set initial weights of the NNs + if recover_training is not None: + NNs_path_rec = str(recover_training[0]) + N_try = recover_training[1] + update_step_counter = recover_training[2] + self.actor_model.load_weights("{}/N_try_{}/actor_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + self.critic_model.load_weights("{}/N_try_{}/critic_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + self.target_critic.load_weights("{}/N_try_{}/target_critic_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + else: + self.target_critic.set_weights(self.critic_model.get_weights()) + + def update(self, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, term_batch, weights_batch, batch_size=None): + ''' Update both critic and actor ''' + # Update the critic backpropagating the gradients + critic_grad, reward_to_go_batch, critic_value, target_critic_value = self.NN.compute_critic_grad(self.critic_model, self.target_critic, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, weights_batch) + self.critic_optimizer.apply_gradients(zip(critic_grad, self.critic_model.trainable_variables)) + + # Update the actor backpropagating the gradients + actor_grad = self.NN.compute_actor_grad(self.actor_model, self.critic_model, state_batch, term_batch, batch_size) + self.actor_optimizer.apply_gradients(zip(actor_grad, self.actor_model.trainable_variables)) + + return reward_to_go_batch, critic_value, target_critic_value + + @tf.function + def update_target(self, target_weights, weights): + ''' Update target critic NN ''' + tau = self.conf.UPDATE_RATE + for (a, b) in zip(target_weights, weights): + a.assign(b * tau + a * (1 - tau)) + + def learn_and_update(self, update_step_counter, buffer, ep): + ''' Sample experience and update buffer priorities and NNs ''' + for i in range(int(self.conf.UPDATE_LOOPS[ep])): + # Sample batch of transitions from the buffer + state_batch, partial_reward_to_go_batch, state_next_rollout_batch, dVdx_batch, d_batch, term_batch, weights_batch, batch_idxes = buffer.sample() + + # Update both critic and actor + reward_to_go_batch, critic_value, target_critic_value = self.update(state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, term_batch, weights_batch) + + # Update buffer priorities + if self.conf.prioritized_replay_alpha != 0: + buffer.update_priorities(batch_idxes, reward_to_go_batch, critic_value, target_critic_value) + + # Update target critic + if not self.conf.MC: + self.update_target(self.target_critic.variables, self.critic_model.variables) + + update_step_counter += 1 + + # Plot rollouts and save the NNs every conf.log_rollout_interval-training episodes + if update_step_counter%self.conf.save_interval == 0: + self.RL_save_weights(update_step_counter) + + return update_step_counter + + def RL_Solve(self, TO_controls, TO_states, TO_step_cost): + ''' Solve RL problem ''' + ep_return = 0 # Initialize the return + rwrd_arr = np.empty(self.NSTEPS_SH+1) # Reward array + state_next_rollout_arr = np.zeros((self.NSTEPS_SH+1, self.conf.nb_state)) # Next state array + partial_reward_to_go_arr = np.empty(self.NSTEPS_SH+1) # Partial cost-to-go array + total_reward_to_go_arr = np.empty(self.NSTEPS_SH+1) # Total cost-to-go array + term_arr = np.zeros(self.NSTEPS_SH+1) # Episode-termination flag array + term_arr[-1] = 1 + done_arr = np.zeros(self.NSTEPS_SH+1) # Episode-MC-termination flag array + + # START RL EPISODE + self.control_arr = TO_controls # action clipped in TO + + if self.conf.env_RL: + for step_counter in range(self.NSTEPS_SH): + # Simulate actions and retrieve next state and compute reward + self.state_arr[step_counter+1,:], rwrd_arr[step_counter] = self.env.step(self.conf.cost_weights_running, self.state_arr[step_counter,:], self.control_arr[step_counter,:]) + + # Compute end-effector position + self.ee_pos_arr[step_counter+1,:] = self.env.get_end_effector_position(self.state_arr[step_counter+1, :]) + rwrd_arr[-1] = self.env.reward(self.conf.cost_weights_terminal, self.state_arr[-1,:]) + else: + self.state_arr, rwrd_arr = TO_states, -TO_step_cost + + ep_return = sum(rwrd_arr) + + # Store transition after computing the (partial) cost-to go when using n-step TD (from 0 to Monte Carlo) + for i in range(self.NSTEPS_SH+1): + # set final lookahead step depending on whether Monte Cartlo or TD(n) is used + if self.conf.MC: + final_lookahead_step = self.NSTEPS_SH + done_arr[i] = 1 + else: + final_lookahead_step = min(i+self.conf.nsteps_TD_N, self.NSTEPS_SH) + if final_lookahead_step == self.NSTEPS_SH: + done_arr[i] = 1 + else: + state_next_rollout_arr[i,:] = self.state_arr[final_lookahead_step+1,:] + + # Compute the partial and total cost to go + partial_reward_to_go_arr[i] = np.float32(sum(rwrd_arr[i:final_lookahead_step+1])) + total_reward_to_go_arr[i] = np.float32(sum(rwrd_arr[i:self.NSTEPS_SH+1])) + + return self.state_arr, partial_reward_to_go_arr, total_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, self.ee_pos_arr + + def RL_save_weights(self, update_step_counter='final'): + ''' Save NN weights ''' + self.actor_model.save_weights(self.conf.NNs_path+"/N_try_{}/actor_{}.h5".format(self.N_try,update_step_counter)) + self.critic_model.save_weights(self.conf.NNs_path+"/N_try_{}/critic_{}.h5".format(self.N_try,update_step_counter)) + self.target_critic.save_weights(self.conf.NNs_path+"/N_try_{}/target_critic_{}.h5".format(self.N_try,update_step_counter)) + + def create_TO_init(self, ep, ICS): + ''' Create initial state and initial controls for TO ''' + self.init_rand_state = ICS + + self.NSTEPS_SH = self.conf.NSTEPS - int(self.init_rand_state[-1]/self.conf.dt) + if self.NSTEPS_SH == 0: + return None, None, None, None, 0 + + # Initialize array to store RL state, control, and end-effector trajectories + self.control_arr = np.empty((self.NSTEPS_SH, self.conf.nb_action)) + self.state_arr = np.empty((self.NSTEPS_SH+1, self.conf.nb_state)) + self.ee_pos_arr = np.empty((self.NSTEPS_SH+1,3)) + + # Set initial state and end-effector position + self.state_arr[0,:] = self.init_rand_state + self.ee_pos_arr[0,:] = self.env.get_end_effector_position(self.state_arr[0, :]) + + # Initialize array to initialize TO state and control variables + init_TO_controls = np.zeros((self.NSTEPS_SH, self.conf.nb_action)) + init_TO_states = np.zeros(( self.NSTEPS_SH+1, self.conf.nb_state)) + + # Set initial state + init_TO_states[0,:] = self.init_rand_state + + # Simulate actor's actions to compute the state trajectory used to initialize TO state variables (use ICS for state and 0 for control if it is the first episode otherwise use policy rollout) + success_init_flag = 1 + for i in range(self.NSTEPS_SH): + if ep == 0: + init_TO_controls[i,:] = np.zeros(self.conf.nb_action) + else: + init_TO_controls[i,:] = tf.squeeze(self.NN.eval(self.actor_model, np.array([init_TO_states[i,:]]))).numpy() + init_TO_states[i+1,:] = self.env.simulate(init_TO_states[i,:],init_TO_controls[i,:]) + if np.isnan(init_TO_states[i+1,:]).any(): + success_init_flag = 0 + return None, None, None, None, success_init_flag + + return self.init_rand_state, init_TO_states, init_TO_controls, self.NSTEPS_SH, success_init_flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_0/TO.py b/Results Single Integrator/Results set test/Code/N_try_0/TO.py new file mode 100644 index 0000000..2468938 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/TO.py @@ -0,0 +1,202 @@ +import sys +import math +import casadi +import numpy as np +import pinocchio.casadi as cpin + +class TO_Casadi: + + def __init__(self, env, conf, env_TO, w_S=0): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param u_min : (float array) Action lower bound array + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param dt : (float) Timestep + + :input system_id : (str) Id system + + :input w_S : (float) Sobolev-training weight + ''' + + self.env = env + self.conf = conf + + self.nx = conf.nx + self.nu = conf.na + + self.w_S = w_S + + self.CAMS = env_TO + + def TO_System_Solve(self, ICS_state, init_TO_states, init_TO_controls, T): + ''' Create and solbe TO casadi problem ''' + ### PROBLEM + opti = casadi.Opti() + + # The control models are stored as a collection of shooting nodes called running models, with an additional terminal model. + self.runningSingleModel = self.CAMS('running_model', self.conf) + runningModels = [ self.runningSingleModel for _ in range(T) ] + self.terminalModel = self.CAMS('terminal_model', self.conf) + + # Decision variables + xs = [ opti.variable(model.nx) for model in runningModels+[self.terminalModel] ] # state variable + us = [ opti.variable(model.nu) for model in runningModels ] # control variable + + # Roll out loop, summing the integral cost and defining the shooting constraints. + total_cost = 0 + + opti.subject_to(xs[0] == ICS_state[:-1]) + + for t in range(T): + x_next, r_cost = runningModels[t].step_fun(xs[t], us[t]) + opti.subject_to(xs[t + 1] == x_next ) + total_cost += r_cost + r_cost_final = self.terminalModel.cost(xs[-1], us[-1]) + total_cost += r_cost_final + + ### SOLVE + opti.minimize(total_cost) + + # Create warmstart + init_x_TO = [np.array(init_TO_states[i,:-1]) for i in range(T+1)] + init_u_TO = [np.array(init_TO_controls[i,:]) for i in range(T)] + + for x,xg in zip(xs,init_x_TO): opti.set_initial(x,xg) + for u,ug in zip(us,init_u_TO): opti.set_initial(u,ug) + + # Set solver options + #opts = {'ipopt.linear_solver':'ma57', 'ipopt.sb': 'yes','ipopt.print_level': 0, 'print_time': 0} #, 'ipopt.max_iter': 500} + opts = {'ipopt.sb': 'yes','ipopt.print_level': 0, 'print_time': 0} #, 'ipopt.max_iter': 500} + opti.solver("ipopt", opts) + + try: + opti.solve() + TO_states = np.array([ opti.value(x) for x in xs ]) + TO_controls = np.array([ opti.value(u) for u in us ]) + TO_total_cost = opti.value(total_cost) + TO_ee_pos_arr = np.empty((T+1,3)) + TO_step_cost = np.empty(T+1) + for n in range(T): + TO_ee_pos_arr[n,:] = np.reshape(runningModels[n].p_ee(TO_states[n,:]),-1) + TO_step_cost[n] = runningModels[n].cost(TO_states[n,:], TO_controls[n,:]) + TO_ee_pos_arr[-1,:] = np.reshape(self.terminalModel.p_ee(TO_states[-1,:]),-1) + TO_step_cost[-1] = self.terminalModel.cost(TO_states[-1,:], TO_controls[-1,:]) + success_flag = 1 + except: + print('ERROR in convergence, returning debug values') + TO_states = np.array([ opti.debug.value(x) for x in xs ]) + TO_controls = np.array([ opti.debug.value(u) for u in us ]) + TO_total_cost = None + TO_ee_pos_arr = None + TO_step_cost = None + success_flag = 0 + + return success_flag, TO_controls, TO_states, TO_ee_pos_arr, TO_total_cost, TO_step_cost + + def TO_Solve(self, ICS_state, init_TO_states, init_TO_controls, T): + ''' Retrieve TO problem solution and compute the value function derviative with respect to the state ''' + success_flag, TO_controls, TO_states, TO_ee_pos_arr, _, TO_step_cost = self.TO_System_Solve(ICS_state, init_TO_states, init_TO_controls, T) + if success_flag == 0: + return None, None, success_flag, None, None, None + + if self.w_S != 0: + # Compute V gradient w.r.t. x (no computation dV/dt) + dVdx = self.backward_pass(T+1, TO_states, TO_controls) + else: + dVdx = np.zeros((T+1, self.conf.nb_state)) + + # Add the last state component (time) + TO_states = np.concatenate((TO_states, init_TO_states[0,-1] + np.transpose(self.conf.dt*np.array([range(T+1)]))), axis=1) + + return TO_controls, TO_states, success_flag, TO_ee_pos_arr, TO_step_cost, dVdx + + def backward_pass(self, T, TO_states, TO_controls, mu=1e-9): + ''' Perform the backward-pass of DDP to obtain the derivatives of the Value function w.r.t x ''' + n = self.conf.nb_state-1 + m = self.conf.nb_action + + X_bar = np.zeros((T, n)) + for i in range(n): + X_bar[:,i] = [TO_states[t,i] for t in range(T)] + + U_bar = np.zeros((T-1, m)) + for i in range(m): + U_bar[:,i] = [TO_controls[t,i] for t in range(T-1)] + + # The task is defined by a quadratic cost: + # sum_{i=0}^T 0.5 x' l_{xx,i} x + l_{x,i} x + 0.5 u' l_{uu,i} u + l_{u,i} u + x' l_{xu,i} u + l_x = np.zeros((T, n)) + l_xx = np.zeros((T, n, n)) + l_u = np.zeros((T-1, m)) + l_uu = np.zeros((T-1, m, m)) + l_xu = np.zeros((T-1, n, m)) + + # The cost-to-go is defined by a quadratic function: 0.5 x' Q_{xx,i} x + Q_{x,i} x + ... + Q_xx = np.zeros((T-1, n, n)) + Q_x = np.zeros((T-1, n)) + Q_uu = np.zeros((T-1, m, m)) + Q_u = np.zeros((T-1, m)) + Q_xu = np.zeros((T-1, n, m)) + + x = casadi.SX.sym('x',n,1) + u = casadi.SX.sym('u',m,1) + + running_cost = -self.runningSingleModel.cost(x, u) + terminal_cost = -self.terminalModel.cost(x, u) + + running_cost_xx, running_cost_x = casadi.hessian(running_cost,x) + running_cost_uu, running_cost_u = casadi.hessian(running_cost,u) + running_cost_xu = casadi.jacobian(casadi.jacobian(running_cost,x),u) + terminal_cost_xx, terminal_cost_x = casadi.hessian(terminal_cost,x) + + fun_running_cost_x = casadi.Function('fun_running_cost_x', [x], [running_cost_x], ['x'], ['running_cost_x']) + fun_running_cost_xx = casadi.Function('fun_running_cost_xx', [x], [running_cost_xx], ['x'], ['running_cost_xx']) + fun_running_cost_xu = casadi.Function('fun_running_cost_xu', [x,u],[running_cost_xu], ['x','u'], ['running_cost_xu']) + fun_running_cost_u = casadi.Function('fun_running_cost_u', [u], [running_cost_u], ['u'], ['running_cost_u']) + fun_running_cost_uu = casadi.Function('fun_running_cost_uu', [u], [running_cost_uu], ['u'], ['running_cost_uu']) + fun_terminal_cost_x = casadi.Function('fun_terminal_cost_x', [x], [terminal_cost_x], ['x'], ['terminal_cost_x']) + fun_terminal_cost_xx = casadi.Function('fun_terminal_cost_xx',[x], [terminal_cost_xx], ['x'], ['terminal_cost_xx']) + + # The Value function is defined by a quadratic function: 0.5 x' V_{xx,i} x + V_{x,i} x + V_xx = np.zeros((T, n, n)) + V_x = np.zeros((T, n+1)) + + # Dynamics derivatives w.r.t. x and u + A = np.zeros((T-1, n, n)) + B = np.zeros((T-1, n, m)) + + # Initialize value function + l_x[-1,:], l_xx[-1,:,:] = np.reshape(fun_terminal_cost_x(X_bar[-1,:]),n), fun_terminal_cost_xx(X_bar[-1,:]) + V_xx[T-1,:,:] = l_xx[-1,:,:] + V_x[T-1,:-1] = l_x[-1,:] + + for i in range(T-2, -1, -1): + # Compute dynamics Jacobians + A[i,:,:], B[i,:,:] = self.env.augmented_derivative(X_bar[i,:], U_bar[i,:]) + + # Compute the gradient of the cost function at X=X_bar + l_x[i,:], l_xx[i,:,:] = np.reshape(fun_running_cost_x(X_bar[i,:]),n), fun_running_cost_xx(X_bar[i,:]) + l_u[i,:],l_uu[i,:,:] = np.reshape(fun_running_cost_u(U_bar[i,:]),m), fun_running_cost_uu(U_bar[i,:]) + l_xu[i,:,:] = fun_running_cost_xu(X_bar[i,:], U_bar[i,:]) + + # Compute regularized cost-to-go + Q_x[i,:] = l_x[i,:] + A[i,:,:].T @ V_x[i+1,:-1] + Q_u[i,:] = l_u[i,:] + B[i,:,:].T @ V_x[i+1,:-1] + Q_xx[i,:,:] = l_xx[i,:,:] + A[i,:,:].T @ V_xx[i+1,:,:] @ A[i,:,:] + Q_uu[i,:,:] = l_uu[i,:,:] + B[i,:,:].T @ V_xx[i+1,:,:] @ B[i,:,:] + Q_xu[i,:,:] = l_xu[i,:,:] + A[i,:,:].T @ V_xx[i+1,:,:] @ B[i,:,:] + + Qbar_uu = Q_uu[i,:,:] + mu*np.identity(m) + Qbar_uu_pinv = np.linalg.pinv(Qbar_uu) + + # Compute the derivative of the Value function w.r.t. x + V_x[i,:-1] = Q_x[i,:] - Q_xu[i,:,:] @ Qbar_uu_pinv @ Q_u[i,:] + V_xx[i,:] = Q_xx[i,:] - Q_xu[i,:,:] @ Qbar_uu_pinv @ Q_xu[i,:,:].T + + return V_x \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_0/conf_car.py b/Results Single Integrator/Results set test/Code/N_try_0/conf_car.py new file mode 100644 index 0000000..c4d72bb --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/conf_car.py @@ -0,0 +1,203 @@ +import math +import numpy as np + +system_id = 'car' + +''' CACTO parameters ''' +EP_UPDATE = 250 # Number of episodes before updating critic and actor +NUPDATES = 260000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 38000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 500 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 10000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 1e2 # Distance from target weight +w_u = 1e1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Car/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Car/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Car/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Car/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Car/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Car/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Car/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' # Link mass + +### State parameters +nb_state = 5 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 5 +na = 2 +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, -math.pi, -10, -3, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, math.pi, 10, 3, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, math.pi, 10, 3, int(NSTEPS*dt)]) # Array used to normalize states +# state: x, y, theta, v, a, t + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-10.0, 10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-10.0, -10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([12.0, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([12.0, -2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([15.0, 0.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +bound_actions = 0 +omega_lower_bound = -2 # Action lower bound +omega_upper_bound = 2 +jerk_lower_bound = -1 +jerk_upper_bound = 1 # Action upper bound +u_min = np.array([omega_lower_bound, jerk_lower_bound]) # Action lower bound vector +u_max = np.array([omega_upper_bound, jerk_upper_bound]) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_0/conf_car_park.py b/Results Single Integrator/Results set test/Code/N_try_0/conf_car_park.py new file mode 100644 index 0000000..05c66c1 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/conf_car_park.py @@ -0,0 +1,221 @@ +import math +import numpy as np + +system_id = 'car_park' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 260000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 38000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/2) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 10000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 0 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -10 # X coord center ellipse 1 +YC1 = 6.75 # Y coord center ellipse 1 +A1 = 17 # Width ellipse 1 +B1 = 4.5 # Height ellipse 1 +XC2 = 10 # X coord center ellipse 2 +YC2 = 6.75 # Y coord center ellipse 2 +A2 = 17 # Width ellipse 2 +B2 = 4.5 # Height ellipse 2 +XC3 = 0 # X coord center ellipse 2 +YC3 = -2 # Y coord center ellipse 2 +A3 = 40 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +L = 4.35 +W = 2 +L_delta = 2.63 +tau_delta = 1 + +check_points_BF = np.array([[-L/2, W/2], + [-L/2+L/3, W/2], + [-L/2+2/3*L, W/2], + [L/2, W/2], + [L/2, 0], + [L/2, -W/2], + [-L/2+2/3*L, -W/2], + [-L/2+L/3, -W/2], + [-L/2, -W/2], + [-L/2, 0]]) # Check points in BF # Check points in BF + +### Weigths +w_d = 1e2 # Distance from target weight +w_u = 1e1 # Control effort weight +w_peak = 1e6 # Target threshold weight +w_ob = 5e4 # Obstacle weight +w_v = 1e2 # Velocity weight +delta_bound = 2*np.pi/6 +w_delta_bound = 0 # Delta bound weight (not implemented) +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u, w_delta_bound]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, w_v, w_ob, w_ob, w_ob, 0, w_delta_bound]) # Terminal cost weights vector + +k_db = 50 + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 1 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = 0 # Target x position +y_des = 6.75 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Car Park/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Car Park/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Car Park/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Car Park/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Car Park/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Car Park/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Car Park/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' # Link mass + +### State parameters +nb_state = 5 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 5 +na = 2 + +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-10, 1.5, -math.pi/6, 0, 0, 0]) #1.5 #-2.5 # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 10, 3, math.pi/6, 0, 0, (NSTEPS-1)*dt]) #3.0 #10 # State upper bound initial configuration array +state_norm_arr = np.array([10, 3, math.pi, 10, math.pi/6, int(NSTEPS*dt)]) # Array used to normalize states +# state: x, y, theta, v, delta, t + +# initial configurations for plot.rollout() +init_states_sim = [np.array([-9.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-5.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-2.5 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([0.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([2.5 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([5.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([9.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +acc_lower_bound = -3 # Action lower bound +acc_upper_bound = 3 +delta_dot_lower_bound = -1 +delta_dot_upper_bound = 1 # Action upper bound +u_min = np.array([acc_lower_bound, delta_dot_lower_bound]) # Action lower bound vector +u_max = np.array([acc_upper_bound, delta_dot_upper_bound]) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-11, 11], [-2.5, 10]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_0/conf_double_integrator.py b/Results Single Integrator/Results set test/Code/N_try_0/conf_double_integrator.py new file mode 100644 index 0000000..03b56ca --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/conf_double_integrator.py @@ -0,0 +1,213 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'double_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 50000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 18000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 200 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-4 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 1 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Double Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Double Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Double Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Double Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Double Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Double Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Double Integrator/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Robot upload data +URDF_FILENAME = "double_integrator.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + +q_init, v_init = np.array([-5, 0]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max) + +### State parameters +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, dt]) # State lower bound vector +x_init_min = np.array([-15, -15, -6, -6, dt]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, 6, 6, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, 6, 6, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([12.0, 2.0, 0.0, 0.0, 0.0]), + np.array([12.0, -2.0, 0.0, 0.0, 0.0]), + np.array([15.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = robot.na # Action size +tau_lower_bound = -2 # Action lower bound +tau_upper_bound = 2 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-15, 15], [-15, 15]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_0/conf_manipulator.py b/Results Single Integrator/Results set test/Code/N_try_0/conf_manipulator.py new file mode 100644 index 0000000..6b5d539 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/conf_manipulator.py @@ -0,0 +1,225 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'manipulator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 380000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 50000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network + nsteps_TD_N = int(NSTEPS/2) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 15000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 0 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 24000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' + +NH1 = 256 # 1st hidden layer size +NH2 = 256 # 2nd hidden layer size + +LR_SCHEDULE = 1 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 1e4 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, w_v, w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 50 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -20.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Manipulator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Manipulator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Manipulator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Manipulator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Manipulator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Manipulator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Manipulator/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Robot upload data +URDF_FILENAME = "planar_manipulator_3dof.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + +q_init, v_init = np.array([math.pi, math.pi, math.pi]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max) + +### System configuration parameters +x_base = -7.0 # x coord base +y_base = 0.0 # y coord base + +### State parameters +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-math.pi, -math.pi, -math.pi, -math.pi/4, -math.pi/4, -math.pi/4, 0]) # State lower bound initial configuration vector +x_max = np.array([ np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ math.pi, math.pi, math.pi, math.pi/4, math.pi/4, math.pi/4, (NSTEPS-1)*dt]) # State upper bound initial configuration vector +state_norm_arr = np.array([15,15,15,10,10,10,int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([math.pi/4, -math.pi/8, -math.pi/8, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, math.pi/8, math.pi/8, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + #np.array([ 1.34565955, -2.39833441, 0.87800266, 0., 0., 0., 0. ]), + #np.array([-1.34565955, 2.39833441, -0.87800266, 0., 0., 0., 0. ]), + np.array([-1.55135003, 2.93707696, -1.3025857 , 0., 0., 0., 0. ]), + np.array([ 1.55135003, -2.93707696, 1.3025857 , 0., 0., 0., 0. ]), + np.array([-1.31811607, 2.63623214, -1.31811607, 0., 0., 0., 0. ]), + np.array([-0.98843209, 1.97686418, -0.98843209, 0., 0., 0., 0. ])] + #np.array([-1.05348883, 1.9057266 , -0.61849459, 0., 0., 0., 0. ])] + #np.array([ 1.05348883, -1.9057266 , 0.61849459, 0., 0., 0., 0. ])] + +### Action parameters +nb_action = robot.na # Action size +tau_lower_bound = -200 # Action lower bound +tau_upper_bound = 200 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-41, 31], [-35, 35]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_0/conf_single_integrator.py b/Results Single Integrator/Results set test/Code/N_try_0/conf_single_integrator.py new file mode 100644 index 0000000..02f767d --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/conf_single_integrator.py @@ -0,0 +1,200 @@ +import numpy as np + +system_id = 'single_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 100000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 25000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Single Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Single Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Single Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Single Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Single Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Single Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = 'set prova b' +NNs_path_rec = './Results Single Integrator/Results {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = 0 +update_step_counter_rec = 12000 + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + + + +### State parameters +nb_state = 2 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 2 +na = 2 +x_min = np.array([-np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0]), + np.array([10.0, 10.0, 0.0]), + np.array([-10.0, 10.0, 0.0]), + np.array([-10.0, -10.0, 0.0]), + np.array([12.0, 2.0, 0.0]), + np.array([12.0, -2.0, 0.0]), + np.array([15.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +tau_lower_bound = -6 # Action lower bound +tau_upper_bound = 6 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_0/conf_ur5.py b/Results Single Integrator/Results set test/Code/N_try_0/conf_ur5.py new file mode 100644 index 0000000..1521ac0 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/conf_ur5.py @@ -0,0 +1,227 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'ur5' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 380000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 50000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + # 2nd hidden layer size + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +# Obstacles parameters +XC1 = 0.0 # X coord center ellipse 1 +YC1 = 0.25 # Y coord center ellipse 1 +ZC1 = 0.2 +ell1_center = [XC1, YC1, ZC1] +A1 = 0.5 # Width ellipse 1 +B1 = 0.2 # Height ellipse 1 +C1 = 0.34 +XC2 = 0.2 # X coord center ellipse 2 +YC2 = 0.425 # Y coord center ellipse 2 +ZC2 = 0.2 +ell2_center = [XC2, YC2, ZC2] +A2 = 0.4 # Width ellipse 2 +B2 = 0.14 # Height ellipse 2 +C2 = 0.34 +XC3 = -0.2 # X coord center ellipse 2 +YC3 = 0.425 # Y coord center ellipse 2 +ZC3 = 0.2 +ell3_center = [XC3, YC3, ZC3] +A3 = 0.4 # Width ellipse 2 +B3 = 0.14 # Height ellipse 2 +C3 = 0.34 +obs_param = np.array([XC1, YC1, ZC1, XC2, YC2, ZC2, XC3, YC3, ZC3, A1, B1, C1, A2, B2, C2, A3, B3, C3]) # Obstacle parameters vector + +# Weigths +w_d = 100 # Distance from target weight +w_u = 1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +# SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +# Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +# Target parameters +x_des = 0.0 # Target x position +y_des = 0.425 # Target y position +z_des = 0.2 +TARGET_STATE = np.array([x_des, y_des, z_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results UR5/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results UR5/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results UR5/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results UR5/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results UR5/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results UR5/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results UR5/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + + + +''' System parameters ''' +# Robot upload data +URDF_FILENAME = "ur5_robot.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +# Dynamics parameters' +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' +use_viewer = False +simulate_real_time = True +show_floor = False +PRINT_T = 1 # print every PRINT_N time steps +DISPLAY_T = 0.02 # update robot configuration in viewer every DISPLAY_N time steps +# CAMERA_TRANSFORM = [3.6914889812469482, 0.4583563506603241, -0.05435386672616005, 0.48037904500961304, 0.5339481830596924, 0.5137122273445129, 0.4692920446395874] +CAMERA_TRANSFORM = [0.36461642384529114, 0.8866147994995117, 2.579286575317383, 0.03584412857890129, 0.14833784103393555, 0.9481053948402405, 0.27893954515457153] + +q_init, v_init = np.array([0.,-math.pi/2,0.,0.,0.,0.]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max, use_viewer, DISPLAY_T, CAMERA_TRANSFORM, show_floor) + +# State parameters +dt = 0.01 # Timestep +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-math.pi, -math.pi, -math.pi, -math.pi, -math.pi, -math.pi, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, 0]) # State lower bound initial configuration vector +x_max = np.array([ np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ math.pi, math.pi, math.pi, math.pi, math.pi, math.pi, math.pi/4, math.pi/4, math.pi/4, math.pi/4, math.pi/4, math.pi/4, (NSTEPS-1)*dt]) # State upper bound initial configuration vector +state_norm_arr = np.array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([math.pi/4, -math.pi/8, -math.pi/8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, math.pi/8, math.pi/8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])] + +# Action parameters +nb_action = robot.na # Action size +u_min = np.array([-150, -150, -150, -28, -28, -28]) # Action lower bound vector +u_max = np.array([150, 150, 150, 28, 28, 28]) # Action upper bound vector +w_b = 1/w_u + + +fig_ax_lim = np.array([[-3, 3], [-3, 3]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + +profile = 0 + +env_RL = 0 diff --git a/Results Single Integrator/Results set test/Code/N_try_0/environment.py b/Results Single Integrator/Results set test/Code/N_try_0/environment.py new file mode 100644 index 0000000..87bd97f --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/environment.py @@ -0,0 +1,816 @@ +import math +import mpmath +import random +import numpy as np +import tensorflow as tf +import pinocchio as pin + +from utils import * + +class Env: + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param simu : (RobotSimulator instance) + :param x_init_min : (float array) State lower bound initial configuration array + :param x_init_max : (float array) State upper bound initial configuration array + :param x_min : (float array) State lower bound vector + :param x_max : (float array) State upper bound vector + :param u_min : (float array) Action lower bound array + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + ''' + + self.conf = conf + + self.nq = conf.nq + self.nv = conf.nv + self.nx = conf.nx + self.nu = conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + def reset(self): + ''' Choose initial state uniformly at random ''' + state = np.zeros(self.conf.nb_state) + + time = random.uniform(self.conf.x_init_min[-1], self.conf.x_init_max[-1]) + for i in range(self.conf.nb_state-1): + state[i] = random.uniform(self.conf.x_init_min[i], self.conf.x_init_max[i]) + state[-1] = self.conf.dt*round(time/self.conf.dt) + + return state + + def check_ICS_feasible(self, state): + ''' Check if ICS is not feasible ''' + # check if ee is in the obstacles + p_ee = self.get_end_effector_position(state) + + ellipse1 = ((p_ee[0] - self.conf.XC1)**2) / ((self.conf.A1 / 2)**2) + ((p_ee[1] - self.conf.YC1)**2) / ((self.conf.B1 / 2)**2) + ellipse2 = ((p_ee[0] - self.conf.XC2)**2) / ((self.conf.A2 / 2)**2) + ((p_ee[1] - self.conf.YC2)**2) / ((self.conf.B2 / 2)**2) + ellipse3 = ((p_ee[0] - self.conf.XC3)**2) / ((self.conf.A3 / 2)**2) + ((p_ee[1] - self.conf.YC3)**2) / ((self.conf.B3 / 2)**2) + + feasible_flag = ellipse1 > 1 and ellipse2 > 1 and ellipse3 > 1 + + return feasible_flag + + def step(self, weights, state, action): + ''' Return next state and reward ''' + # compute next state + state_next = self.simulate(state, action) + + # compute reward + reward = self.reward(weights, state, action) + + return (state_next, reward) + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + # Simulate control action + self.conf.simu.simulate(np.copy(state[:-1]), action, self.conf.dt, 1) ### Explicit Euler ### + + # Return next state + state_next[:self.nq], state_next[self.nq:self.nx] = np.copy(self.conf.simu.q), np.copy(self.conf.simu.v) + state_next[-1] = state[-1] + self.conf.dt + + return state_next + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Create robot model in Pinocchio with q_init as initial configuration + q_init = state[:self.nq] + v_init = state[self.nq:self.nx] + + # Dynamics gradient w.r.t control (1st order euler) + pin.computeABADerivatives(self.conf.robot.model, self.conf.robot.data, np.copy(q_init), np.copy(v_init), action) + + Fu = np.zeros((self.nx+1, self.nu)) + Fu[self.nv:-1, :] = self.conf.robot.data.Minv + Fu[:self.nx, :] *= self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + q = state[:self.nq] + v = state[self.nq:self.nx] + + # Compute Jacobians for continuous time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + pin.computeABADerivatives(self.conf.robot.model, self.conf.robot.data, q, v, action) + + Fx[:self.nv, :self.nv] = 0.0 + Fx[:self.nv, self.nv:self.nx] = np.identity(self.nv) + Fx[self.nv:self.nx, :self.nv] = self.conf.robot.data.ddq_dq + Fx[self.nv:self.nx, self.nv:self.nx] = self.conf.robot.data.ddq_dv + Fu[self.nv:self.nx, :] = self.conf.robot.data.Minv + + # Convert them to discrete time + Fx = np.identity(self.conf.nb_state-1) + self.conf.dt * Fx + Fu *= self.conf.dt + + return Fx, Fu + + def simulate_batch(self, state, action): + ''' Simulate dynamics using tensors and compute its gradient w.r.t control. Batch-wise computation ''' + state_next = np.array([self.simulate(s, a) for s, a in zip(state, action)]) + + return tf.convert_to_tensor(state_next, dtype=tf.float32) + + def derivative_batch(self, state, action): + ''' Simulate dynamics using tensors and compute its gradient w.r.t control. Batch-wise computation ''' + Fu = np.array([self.derivative(s, a) for s, a in zip(state, action)]) + + return tf.convert_to_tensor(Fu, dtype=tf.float32) + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + q = state[:self.nq] + + RF = self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id) + + H = self.conf.robot.framePlacement(q, RF, recompute) + + p = H.translation + + return p + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + +class SingleIntegrator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[0,0] = self.conf.dt + Fu[1,1] = self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0], + [0, 1]]) + + Fu[0,0] = self.conf.dt + Fu[1,1] = self.conf.dt + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*action[0] + state_next[1] = state[1] + self.conf.dt*action[1] + state_next[2] = state[2] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + + return p + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class DoubleIntegrator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = [self.get_end_effector_position(state)[i] for i in range(2)] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = np.math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class Car(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[2,0] = self.conf.dt + Fu[4,1] = self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0, -self.conf.dt*state[3]*math.sin(state[2]) -self.conf.dt**2*state[4]*math.sin(state[2])/2, self.conf.dt*math.cos(state[2]), self.conf.dt**2*math.cos(state[2])/2], + [0, 1, self.conf.dt*state[3]*math.cos(state[2]) +self.conf.dt**2*state[4]*math.cos(state[2])/2, self.conf.dt*math.sin(state[2]), self.conf.dt**2*math.sin(state[2])/2], + [0, 0, 1, 0, 0], + [0, 0, 0, 1, self.conf.dt], + [0, 0, 0, 0, 1]]) + + Fu[2,0] = self.conf.dt + Fu[4,1] = self.conf.dt + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*state[3]*tf.cos(state[2]) + self.conf.dt**2*state[4]*tf.cos(state[2])/2 + state_next[1] = state[1] + self.conf.dt*state[3]*tf.sin(state[2]) + self.conf.dt**2*state[4]*tf.sin(state[2])/2 + state_next[2] = state[2] + self.conf.dt*action[0] + state_next[3] = state[3] + self.conf.dt*state[4] + state_next[4] = state[4] + self.conf.dt*action[1] + state_next[5] = state[5] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + + return p + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class CarPark(Car): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def check_ICS_feasible(self, state): + ''' Check if ICS is not feasible ''' + # check if ee is in the obstacles + x_ee, y_ee = self.get_end_effector_position(state)[:2] + theta_ee = state[2] + + for i in range(len(self.conf.check_points_BF)): + check_points_WF_i = np.array([x_ee, y_ee]) + np.array([[math.cos(theta_ee), -math.sin(theta_ee)], [math.sin(theta_ee), math.cos(theta_ee)]]).dot(self.conf.check_points_BF[i,:]) + obs_1 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC1,self.YC1,self.A1,self.B1) + obs_2 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC2,self.YC2,self.A2,self.B2) + obs_3 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC3,self.YC3,self.A3,self.B3) + + feasible_flag = obs_1 < 0.5 and obs_2 < 0.5 and obs_3 < 0.5 + if feasible_flag == 0: + return feasible_flag + + return feasible_flag + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[3,0] = self.conf.dt + Fu[4,1] = self.conf.dt/self.conf.tau_delta + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0, -self.conf.dt*state[3]*math.sin(state[2]), self.conf.dt*math.cos(state[2]), 0], + [0, 1, self.conf.dt*state[3]*math.cos(state[2]), self.conf.dt*math.sin(state[2]), 0], + [0, 0, 1, self.conf.dt*math.tan(state[4])/self.conf.L_delta, self.conf.dt*state[3]*mpmath.sec(state[4])**2/self.conf.L_delta], + [0, 0, 0, 1, 0], + [0, 0, 0, 0, 1]]) + + Fu[3,0] = self.conf.dt + Fu[4,1] = self.conf.dt/self.conf.tau_delta + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*state[3]*math.cos(state[2]) + state_next[1] = state[1] + self.conf.dt*state[3]*math.sin(state[2]) + state_next[2] = state[2] + self.conf.dt*state[3]*math.tan(state[4])/self.conf.L_delta + state_next[3] = state[3] + self.conf.dt*action[0] + state_next[4] = state[4] + self.conf.dt*action[1]/self.conf.tau_delta + state_next[5] = state[5] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + np.array([[math.cos(state[2]), -math.sin(state[2])], [math.sin(state[2]), math.cos(state[2])]]).dot(np.array([self.conf.L_delta/2,0])) + + return p + + def obs_cost_fun(self,x,y,x_step,y_step,Wx,Wy,fv=1,k=50): + k = self.conf.k_db + + term1 = 4 + 4 * (y - y_step + Wy/2)**2 * k**2 + term2 = 4 + 4 * (y - y_step - Wy/2)**2 * k**2 + term3 = 4 + 4 * (x - x_step + Wx/2)**2 * k**2 + term4 = 4 + 4 * (x - x_step - Wx/2)**2 * k**2 + obs_cost = (term1)**(-1/2) * fv * (-np.sqrt(term2) / 2 + (y - y_step - Wy/2) * k) * (term3)**(-1/2) * (term2)**(-1/2) * (np.sqrt(term1) / 2 + (y - y_step + Wy/2) * k) * (term4)**(-1/2) * (np.sqrt(term3) / 2 + (x - x_step + Wx/2) * k) * (-np.sqrt(term4) / 2 + (x - x_step - Wx/2) * k) + + return obs_cost + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + theta_ee = state[2] + + obs_cost = 0 + check_points_WF = np.dot(np.array([[np.cos(theta_ee), -np.sin(theta_ee)], [np.sin(theta_ee), np.cos(theta_ee)]]), self.conf.check_points_BF.T).T + np.array([x_ee, y_ee]) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC1, self.YC1, self.A1, self.B1)) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC2, self.YC2, self.A2, self.B2)) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC3, self.YC3, self.A3, self.B3)) + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 + 0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 + 0.1) - math.sqrt(0.1) - 0.1 )) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*state[3]**2 - weights[3]*obs_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class Manipulator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = [self.get_end_effector_position(state)[i] for i in range(2)] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term penalizing the FINAL joint velocity + if weights[2] != 0: + vel_cost = state[self.nq:self.nx].dot(state[self.nq:self.nx]) + else: + vel_cost = 0 + + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*vel_cost - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) #- weights[2]*vel_cost + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class UR5(Env): + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.ZC1 = self.conf.obs_param[2] + self.XC2 = self.conf.obs_param[3] + self.YC2 = self.conf.obs_param[4] + self.ZC2 = self.conf.obs_param[5] + self.XC3 = self.conf.obs_param[6] + self.YC3 = self.conf.obs_param[7] + self.ZC3 = self.conf.obs_param[8] + + self.A1 = self.conf.obs_param[9] + self.B1 = self.conf.obs_param[10] + self.C1 = self.conf.obs_param[11] + self.A2 = self.conf.obs_param[12] + self.B2 = self.conf.obs_param[13] + self.C2 = self.conf.obs_param[14] + self.A3 = self.conf.obs_param[15] + self.B3 = self.conf.obs_param[16] + self.C3 = self.conf.obs_param[17] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee, z_ee = self.get_end_effector_position(state) + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) + ((z_ee-self.ZC1)**2)/((self.C1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) + ((z_ee-self.ZC2)**2)/((self.C2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) + ((z_ee-self.ZC3)**2)/((self.C3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((z_ee-self.TARGET_STATE[2])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + if action is not None: + u_cost = action.dot(action) + else: + u_cost = 0 + + # Term penalizing the FINAL joint velocity + vel_cost = state[self.nq:self.nx].dot(state[self.nq:self.nx]) + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + (z_ee-self.TARGET_STATE[2])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*vel_cost - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) diff --git a/Results Single Integrator/Results set test/Code/N_try_0/environment_TO.py b/Results Single Integrator/Results set test/Code/N_try_0/environment_TO.py new file mode 100644 index 0000000..0cf4b63 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/environment_TO.py @@ -0,0 +1,778 @@ +import sys +import math +import casadi +import numpy as np +import pinocchio.casadi as cpin + +class SingleIntegrator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + p_ee[-1] = 0 + + return p_ee + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*u[0] + x_next[1] = x[1] + self.conf.dt*u[1] + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class DoubleIntegrator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class Car_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + p_ee[-1] = 0 + + return p_ee + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*x[3]*casadi.cos(x[2]) + self.conf.dt**2*x[4]*casadi.cos(x[2])/2 + x_next[1] = x[1] + self.conf.dt*x[3]*casadi.sin(x[2]) + self.conf.dt**2*x[4]*casadi.sin(x[2])/2 + x_next[2] = x[2] + self.conf.dt*u[0] + x_next[3] = x[3] + self.conf.dt*x[4] + x_next[4] = x[4] + self.conf.dt*u[1] + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class CarPark_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + np.array([[np.cos(cx[2]), -np.sin(cx[2])], [np.sin(cx[2]), np.cos(cx[2])]]).dot(np.array([self.conf.L_delta/2,0])) + p_ee[-1] = 0 + + return p_ee + + def obs_cost_fun(self, x, y, x_clip, y_clip, Wx, Wy, fv=1, k=50): + k = self.conf.k_db + obs_cost = (4 + 4 * (y - y_clip + Wy / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * fv * (-np.sqrt(4 + 4 * (y - y_clip - Wy / 2) ** 2 * k ** 2) / 2 + (y - y_clip - Wy / 2) * k) * (4 + 4 * (x - x_clip + Wx / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (4 + 4 * (y - y_clip - Wy / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (np.sqrt(4 + 4 * (y - y_clip + Wy / 2) ** 2 * k ** 2) / 2 + (y - y_clip + Wy / 2) * k) * (4 + 4 * (x - x_clip - Wx / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (np.sqrt(4 + 4 * (x - x_clip + Wx / 2) ** 2 * k ** 2) / 2 + (x - x_clip + Wx / 2) * k) * (-np.sqrt(4 + 4 * (x - x_clip - Wx / 2) ** 2 * k ** 2) / 2 + (x - x_clip - Wx / 2) * k) + + return obs_cost + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def rotation_matrix(self, angle): + ''' Compute the 2x2 rotation matrix for a given angle ''' + cos_theta = casadi.cos(angle) + sin_theta = casadi.sin(angle) + + rotation_mat = casadi.vertcat(casadi.horzcat(cos_theta, -sin_theta), + casadi.horzcat(sin_theta, cos_theta)) + return rotation_mat + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee_tmp = self.p_ee(x) + p_ee = p_ee_tmp[:2] + theta_ee = x[2] + + ### Penalties representing the obstacle ### + obs_cost = 0 + check_points_WF = casadi.mtimes(self.rotation_matrix(theta_ee), self.conf.check_points_BF.T).T + casadi.repmat(p_ee.T, self.conf.check_points_BF.shape[0], 1) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC1, self.YC1, self.A1, self.B1)) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC2, self.YC2, self.A2, self.B2)) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC3, self.YC3, self.A3, self.B3)) + + ### Control effort term ### + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 2*0.1 - 2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*x[3]**2 + self.weights[3]*obs_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*x[3]*casadi.cos(x[2]) + x_next[1] = x[1] + self.conf.dt*x[3]*casadi.sin(x[2]) + x_next[2] = x[2] + self.conf.dt*x[3]*casadi.tan(x[4])/self.conf.L_delta + x_next[3] = x[3] + self.conf.dt*u[0] + x_next[4] = x[4] + self.conf.dt*u[1]/self.conf.tau_delta + + return x_next + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class Manipulator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class UR5_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.ZC1 = self.conf.obs_param[2] + self.XC2 = self.conf.obs_param[3] + self.YC2 = self.conf.obs_param[4] + self.ZC2 = self.conf.obs_param[5] + self.XC3 = self.conf.obs_param[6] + self.YC3 = self.conf.obs_param[7] + self.ZC3 = self.conf.obs_param[8] + + self.A1 = self.conf.obs_param[9] + self.B1 = self.conf.obs_param[10] + self.C1 = self.conf.obs_param[11] + self.A2 = self.conf.obs_param[12] + self.B2 = self.conf.obs_param[13] + self.C2 = self.conf.obs_param[14] + self.A3 = self.conf.obs_param[15] + self.B3 = self.conf.obs_param[16] + self.C3 = self.conf.obs_param[17] + + self.TARGET_STATE = self.conf.TARGET_STATE + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC1)**2)/((self.conf.A1/2)**2) + ((p_ee[1]-self.conf.YC1)**2)/((self.conf.B1/2)**2) + ((p_ee[2]-self.conf.ZC1)**2)/((self.conf.C1/2)**2) - 1.0)) + 1)/self.conf.alpha + ell2_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC2)**2)/((self.conf.A2/2)**2) + ((p_ee[1]-self.conf.YC2)**2)/((self.conf.B2/2)**2) + ((p_ee[2]-self.conf.ZC2)**2)/((self.conf.C2/2)**2) - 1.0)) + 1)/self.conf.alpha + ell3_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC3)**2)/((self.conf.A3/2)**2) + ((p_ee[1]-self.conf.YC3)**2)/((self.conf.B3/2)**2) + ((p_ee[2]-self.conf.ZC3)**2)/((self.conf.C3/2)**2) - 1.0)) + 1)/self.conf.alpha + + ### Control effort term ### + u_cost = 0 + for i in range(self.nu): + u_cost += u[i]**2 + self.conf.w_b*(u[i]/self.conf.u_max[i])**10 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.conf.alpha2*-(np.sqrt((p_ee[0]-self.conf.TARGET_STATE[0])**2 +0.1) - np.sqrt(0.1) - 0.1 + np.sqrt((p_ee[1]-self.conf.TARGET_STATE[1])**2 +0.1) - np.sqrt(0.1) - 0.1 + np.sqrt((p_ee[2]-self.conf.TARGET_STATE[2])**2 +0.1) - np.sqrt(0.1) - 0.1)) + 1)/self.conf.alpha2 + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.conf.TARGET_STATE[0])**2 + (p_ee[1]-self.conf.TARGET_STATE[1])**2 + (p_ee[2]-self.conf.TARGET_STATE[2])**2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_0/generate_tests_set_script.py b/Results Single Integrator/Results set test/Code/N_try_0/generate_tests_set_script.py new file mode 100644 index 0000000..4c5f781 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/generate_tests_set_script.py @@ -0,0 +1,26 @@ +### Create file to be executed ### +import os +import shutil + +system_id = 'car' +system_id_short = 'C' + +n_runs = 10 + +w_S = 1e-3 +info = '{}'.format(1) + +offset = 0 if w_S ==0 else 100 + +seed_list = [29556, 5280, 739, 92, 10, 7298, 14, 264, 22135, 342] + +shutil.copy('Template.sh', '{}{}.sh'.format(system_id_short,info)) +with open('{}{}.sh'.format(system_id_short,info), 'a') as f: + for i in range(1,1 + n_runs): + f.write("nohup python3 -u main.py --system-id='{}' --test-n={} --seed={} --w-S={} > out/{}{}.txt &\n".format(system_id,offset+i,seed_list[i-1], w_S, system_id_short,offset+i)) + f.write("\necho 'Running scripts in parallel'\n") + f.write("echo 'PID of this script is:' $$\n") + f.write("wait\n") + f.write("echo 'Script done running'\n") + +os.makedirs('out', exist_ok=True) \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_0/main.py b/Results Single Integrator/Results set test/Code/N_try_0/main.py new file mode 100644 index 0000000..8121e8c --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/main.py @@ -0,0 +1,254 @@ +import os +import sys +import time +import shutil +import random +import argparse +import importlib +import numpy as np +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # {'0' -> show all logs, '1' -> filter out info, '2' -> filter out warnings} +import tensorflow as tf +from multiprocessing import Pool +from RL import RL_AC +from TO import TO_Casadi +from plot_utils import PLOT +from NeuralNetwork import NN +from replay_buffer import PrioritizedReplayBuffer, ReplayBuffer + +def parse_args(): + ''' Parse the arguments for CACTO training ''' + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + + parser.add_argument('--test-n', type=int, default=0, + help="Test number") + + parser.add_argument('--seed', type=int, default=0, + help="random and tf.random seed") + + parser.add_argument('--system-id', type=str, default='single_integrator', + choices=["single_integrator", "double_integrator", "car", "car_park", "manipulator", "ur5"], + help="System-id (single_integrator, double_integrator, car, manipulator, ur5") + + parser.add_argument('--recover-training-flag', type=bool, default=False, + choices=["True", "False"], + help="Flag to recover training") + ### Not tested ### + #parser.add_argument('--GPU-flag', type=bool, default=False, + # choices=["True", "False"], + # help="Flag to use GPU") + + parser.add_argument('--nb-cpus', type=int, default=10, + help="Number of TO problems solved in parallel") + + parser.add_argument('--w-S', type=float, default=0, + help="Sobolev training - weight of the value related error") + + args = parser.parse_args() + dict_args = vars(args) + + return dict_args + + + + +if __name__ == '__main__': + + args = parse_args() + + ############### Input ############### + N_try = args['test_n'] + + if args['seed'] == None: + seed = random.randint(1,100000) + else: + seed = args['seed'] + tf.random.set_seed(seed) # Set tensorflow seed + random.seed(seed) # Set random seed + + system_id = args['system_id'] + + recover_training_flag = args['recover_training_flag'] + + ### Not tested ### + #GPU_flag = args['GPU_flag'] + #if GPU_flag: + # os.environ["CUDA_VISIBLE_DEVICES"]="-1" + #print(tf.config.experimental.list_physical_devices('GPU')) + + nb_cpus = args['nb_cpus'] + + w_S = args['w_S'] + ######################################################### + + + + # Import configuration file and environment file + system_map = { + 'single_integrator': ('conf_single_integrator', 'SingleIntegrator', 'SingleIntegrator_CAMS'), + 'double_integrator': ('conf_double_integrator', 'DoubleIntegrator', 'DoubleIntegrator_CAMS'), + 'car': ('conf_car', 'Car', 'Car_CAMS'), + 'car_park': ('conf_car_park', 'CarPark', 'CarPark_CAMS'), + 'manipulator': ('conf_manipulator', 'Manipulator', 'Manipulator_CAMS'), + 'ur5': ('conf_ur5', 'UR5', 'UR5_CAMS') + } + try: + conf_module, env_class, env_TO_class = system_map[system_id] + conf = importlib.import_module(conf_module) + Environment = getattr(importlib.import_module('environment'), env_class) + Environment_TO = getattr(importlib.import_module('environment_TO'), env_TO_class) + except KeyError: + print('System {} not found'.format(system_id)) + sys.exit() + + # Create folders to store the results and the trained NNs + for path in conf.path_list: + os.makedirs(path + '/N_try_{}'.format(N_try), exist_ok=True) + os.makedirs(conf.Config_path, exist_ok=True) + + # Save configuration + params = [p for p in conf.__dict__ if not p.startswith("__")] + with open(conf.Config_path + '/config{}.txt'.format(N_try), 'w') as f: + for p in params: + f.write('{} = {}\n'.format(p, conf.__dict__[p])) + f.write('Seed = {}\n'.format(seed)) + f.write('w_S = {}'.format(w_S)) + + shutil.copy('{}.py'.format(conf_module), conf.Config_path + '/' + conf_module + '_{}.py'.format(N_try)) + with open(conf.Config_path + '/' + conf_module + '_{}.py'.format(N_try), 'a') as f: + f.write('\n\n# {}'.format(args)) + + # Copy all file with .py extension from /mydir to /mydestdir + for file in os.listdir("./"): + if file.endswith(".py"): + shutil.copy(os.path.join("./", file), os.path.join(conf.Code_path + '/N_try_{}'.format(N_try), file)) + + # Create empty txt file in Log_path to store the test info + open(conf.Log_path + '/info.txt', 'a').close() + + + + ### Create instances of the used classes ### + env = Environment(conf) # Create environment instances + env_TO = Environment_TO + NN_inst = NN(env, conf, w_S) # Create NN instance + TrOp = TO_Casadi(env, conf, env_TO, w_S) # Create TO instance + RLAC = RL_AC(env, NN_inst, conf, N_try) # Create RL instance + buffer = ReplayBuffer(conf) if conf.prioritized_replay_alpha == 0 else PrioritizedReplayBuffer(conf) # Create an empty (prioritized) replay buffer + plot_fun = PLOT(N_try, env, NN_inst, conf) # Create PLOT instance + + # Set initial weights of the NNs, initialize the counter of the updates and setup NN models + if recover_training_flag: + recover_training = np.array([conf.NNs_path_rec, conf.N_try_rec, conf.update_step_counter_rec]) + update_step_counter = conf.update_step_counter_rec + + RLAC.setup_model(recover_training) + else: + update_step_counter = 0 + + RLAC.setup_model() + + # Save initial weights of the NNs + RLAC.RL_save_weights(update_step_counter) + + # Plot initial rollouts + plot_fun.plot_traj_from_ICS(np.array(conf.init_states_sim), TrOp, RLAC, update_step_counter=update_step_counter,steps=conf.NSTEPS, init=0) + + # Initialize arrays to store the reward history of each episode and the average reward history of last 100 episodes + ep_arr_idx = 0 + ep_reward_arr = np.zeros(conf.NEPISODES-ep_arr_idx)*np.nan + + def compute_sample(args): + ''' Create samples solving TO problems starting from given ICS ''' + ep = args[0] + ICS = args[1] + + # Create initial TO # + init_rand_state, init_TO_states, init_TO_controls, NSTEPS_SH, success_init_flag = RLAC.create_TO_init(ep, ICS) + if success_init_flag == 0: + return None + + # Solve TO problem # + TO_controls, TO_states, success_flag, TO_ee_pos_arr, TO_step_cost, dVdx = TrOp.TO_Solve(init_rand_state, init_TO_states, init_TO_controls, NSTEPS_SH) + if success_flag == 0: + return None + + # Collect experiences + state_arr, partial_reward_to_go_arr, total_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, RL_ee_pos_arr = RLAC.RL_Solve(TO_controls, TO_states, TO_step_cost) + + if conf.env_RL == 0: + RL_ee_pos_arr = TO_ee_pos_arr + + return NSTEPS_SH, TO_controls, TO_ee_pos_arr, dVdx, state_arr.tolist(), partial_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, RL_ee_pos_arr + + def create_unif_TO_init(n_UICS=1): + ''' Create n uniformely distributed ICS ''' + # Create ICS TO # + init_rand_state = env.reset() + + return init_rand_state + + + + ### START TRAINING ### + if conf.profile: + import cProfile, pstats + + profiler = cProfile.Profile() + profiler.enable() + + time_start = time.time() + + for ep in range(conf.NLOOPS): + # Generate and store conf.EP_UPDATE random-uniform ICS + with Pool(nb_cpus) as p: + init_rand_state = p.map(create_unif_TO_init, range(conf.EP_UPDATE)) + + # Generate samples + with Pool(nb_cpus) as p: + tmp = p.map(compute_sample, zip(ep*np.ones(conf.EP_UPDATE), init_rand_state)) + + # Remove unsuccessful TO problems and update EP_UPDATE + tmp = [x for x in tmp if x is not None] + NSTEPS_SH, TO_controls, ee_pos_arr_TO, dVdx, state_arr, partial_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, ee_pos_arr_RL = zip(*tmp) + + # Update the buffer + buffer.add(state_arr, partial_reward_to_go_arr, state_next_rollout_arr, dVdx, done_arr, term_arr) + + # Update NNs + update_step_counter = RLAC.learn_and_update(update_step_counter, buffer, ep) + + # plot Critic value function + #plot_fun.plot_Critic_Value_function(RLAC.critic_model, update_step_counter, system_id) ### + + # Plot rollouts and state and control trajectories + if update_step_counter%conf.plot_rollout_interval_diff_loc == 0 or system_id == 'single_integrator' or system_id == 'double_integrator' or system_id == 'car_park' or system_id == 'car' or system_id == 'manipulator': + print("System: {} - N_try = {}".format(conf.system_id, N_try)) + plot_fun.plot_Critic_Value_function(RLAC.critic_model, update_step_counter, system_id) + plot_fun.plot_traj_from_ICS(np.array(conf.init_states_sim), TrOp, RLAC, update_step_counter=update_step_counter, ep=ep,steps=conf.NSTEPS, init=1) + + # Update arrays to store the reward history and its average + ep_reward_arr[ep_arr_idx:ep_arr_idx+len(tmp)] = ep_return + ep_arr_idx += len(tmp) + + for i in range(len(tmp)): + print("Episode {} ---> Return = {}".format(ep*len(tmp) + i, ep_return[i])) + + if update_step_counter > conf.NUPDATES: + break + + time_end = time.time() + print('Elapsed time: ', time_end-time_start) + + if conf.profile: + profiler.disable() + stats = pstats.Stats(profiler).sort_stats('cumtime') + stats.print_stats() + + # Plot returns + plot_fun.plot_Return(ep_reward_arr) + + # Save networks at the end of the training + RLAC.RL_save_weights() + + # Simulate the final policy + plot_fun.rollout(update_step_counter, RLAC.actor_model, conf.init_states_sim) diff --git a/Results Single Integrator/Results set test/Code/N_try_0/plot_utils.py b/Results Single Integrator/Results set test/Code/N_try_0/plot_utils.py new file mode 100644 index 0000000..55daab0 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/plot_utils.py @@ -0,0 +1,714 @@ +import math +import numpy as np +import tensorflow as tf +import matplotlib.pyplot as plt +from matplotlib import cm, colors +from matplotlib.patches import Ellipse, FancyBboxPatch, Rectangle +from matplotlib.transforms import Affine2D +import mpl_toolkits.mplot3d.art3d as art3d + +class PLOT(): + def __init__(self, N_try, env, NN, conf): + ''' + :input N_try : (Test number) + + :input env : (Environment instance) + + :input conf : (Configuration file) + :param fig_ax_lim : (float array) Figure axis limit [x_min, x_max, y_min, y_max] + :param Fig_path : (str) Figure path + :param NSTEPS : (int) Max episode length + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param NORMALIZE_INPUTS : (bool) Flag to normalize inputs (state) + :param state_norm_array : (float array) Array used to normalize states + :param dt : (float) Timestep + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + ''' + self.env = env + self.NN = NN + self.conf = conf + + self.N_try = N_try + + self.xlim = conf.fig_ax_lim[0].tolist() + self.ylim = conf.fig_ax_lim[1].tolist() + + # Set the ticklabel font size globally + plt.rcParams['xtick.labelsize'] = 22 + plt.rcParams['ytick.labelsize'] = 22 + plt.rcParams.update({'font.size': 20}) + + return + + def plot_obstaces(self, a=1): + if self.conf.system_id == 'car_park': + obs1 = Rectangle((self.conf.XC1-self.conf.A1/2, self.conf.YC1-self.conf.B1/2), self.conf.A1, self.conf.B1, 0.0,alpha=a) + obs1.set_facecolor([30/255, 130/255, 76/255, 1]) + obs2 = Rectangle((self.conf.XC2-self.conf.A2/2, self.conf.YC2-self.conf.B2/2), self.conf.A2, self.conf.B2, 0.0,alpha=a) + obs2.set_facecolor([30/255, 130/255, 76/255, 1]) + obs3 = Rectangle((self.conf.XC3-self.conf.A3/2, self.conf.YC3-self.conf.B3/2), self.conf.A3, self.conf.B3, 0.0,alpha=a) + obs3.set_facecolor([30/255, 130/255, 76/255, 1]) + + #rec1 = FancyBboxPatch((self.conf.XC1-self.conf.A1/2, self.conf.YC1-self.conf.B1/2), self.conf.A1, self.conf.B1,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec1.set_facecolor([30/255, 130/255, 76/255, 1]) + #rec2 = FancyBboxPatch((self.conf.XC2-self.conf.A2/2, self.conf.YC2-self.conf.B2/2), self.conf.A2, self.conf.B2,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec2.set_facecolor([30/255, 130/255, 76/255, 1]) + #rec3 = FancyBboxPatch((self.conf.XC3-self.conf.A3/2, self.conf.YC3-self.conf.B3/2), self.conf.A3, self.conf.B3,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec3.set_facecolor([30/255, 130/255, 76/255, 1]) + else: + obs1 = Ellipse((self.conf.XC1, self.conf.YC1), self.conf.A1, self.conf.B1, 0.0,alpha=a) + obs1.set_facecolor([30/255, 130/255, 76/255, 1]) + obs2 = Ellipse((self.conf.XC2, self.conf.YC2), self.conf.A2, self.conf.B2, 0.0,alpha=a) + obs2.set_facecolor([30/255, 130/255, 76/255, 1]) + obs3 = Ellipse((self.conf.XC3, self.conf.YC3), self.conf.A3, self.conf.B3, 0.0,alpha=a) + obs3.set_facecolor([30/255, 130/255, 76/255, 1]) + + return [obs1, obs2, obs3] + + def plot_Reward(self, plot_obs=0): + x = np.arange(-15, 15, 0.1) + y = np.arange(-10, 10, 0.1) + theta = np.pi/2 + ICS = np.array([np.array([i,j,0]) for i in x for j in y]) + state = np.array([self.compute_ICS(np.array([i,j,0]), 'car')[0] for i in x for j in y]) # for k in theta] + state[:,2] = theta + r = [self.env.reward(self.conf.cost_weights_running, s) for s in state] + mi = min(r) + ma = max(r) + norm = colors.Normalize(vmin=mi,vmax=ma) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + pti = ax.scatter(ICS[:,0], ICS[:,1], norm=norm, c=r, cmap=cm.get_cmap('hot_r')) + plt.colorbar(pti) + + if plot_obs: + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + + # Center and check points of 'car_park' system + #check_points_WF_i = np.array([[math.cos(theta), -math.sin(theta)], [math.sin(theta), math.cos(theta)]]).dot(self.conf.check_points_BF[0,:]) + ICS[0,:2] + #ax.scatter(check_points_WF_i[0], check_points_WF_i[1], c='b') + #for i in range(1,len(self.conf.check_points_BF)): + # check_points_WF_i = np.array([[math.cos(theta), -math.sin(theta)], [math.sin(theta), math.cos(theta)]]).dot(self.conf.check_points_BF[i,:]) + ICS[0,:2] + # ax.scatter(check_points_WF_i[0], check_points_WF_i[1], c='r') + + ax.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5, legend='Goal position') + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.set_title('Plane') + #ax.legend() + ax.grid(True) + plt.show() + + def compute_ICS(self, p_ee, sys_id, theta=None, continue_flag=0): + if sys_id == 'manipulator': + radius = math.sqrt((p_ee[0]-self.conf.x_base)**2+(p_ee[1])**2) + if radius > 30: + continue_flag = 1 + return None, continue_flag + + phi = math.atan2(p_ee[1]-self.conf.y_base,(p_ee[0]-self.conf.x_base)) # SUM OF THE ANGLES FIXED + X3rd_joint = (p_ee[0]-self.conf.x_base) - self.conf.l* math.cos(phi) + Y3rd_joint = (p_ee[1]-self.conf.y_base) - self.conf.l* math.sin(phi) + + if abs(X3rd_joint) <= 1e-6 and abs(Y3rd_joint) <= 1e-6: + continue_flag = 1 + return None, continue_flag + + c2 = (X3rd_joint**2 + Y3rd_joint**2 -2*self.conf.l**2)/(2*self.conf.l**2) + + if p_ee[1] >= 0: + s2 = math.sqrt(1-c2**2) + else: + s2 = -math.sqrt(1-c2**2) + + s1 = ((self.conf.l + self.conf.l*c2)*Y3rd_joint - self.conf.l*s2*X3rd_joint)/(X3rd_joint**2 + Y3rd_joint**2) + c1 = ((self.conf.l + self.conf.l*c2)*X3rd_joint - self.conf.l*s2*Y3rd_joint)/(X3rd_joint**2 + Y3rd_joint**2) + ICS_q0 = math.atan2(s1,c1) + ICS_q1 = math.atan2(s2,c2) + ICS_q2 = phi-ICS_q0-ICS_q1 + + ICS = np.array([ICS_q0, ICS_q1, ICS_q2, 0.0, 0.0, 0.0, 0.0]) + + elif sys_id == 'car': + if theta == None: + theta = 0*np.random.uniform(-math.pi,math.pi) + ICS = np.array([p_ee[0], p_ee[1], theta, 0.0, 0.0, 0.0]) + + elif sys_id == 'car_park': + if theta == None: + #theta = 0*np.random.uniform(-math.pi,math.pi) + theta = np.pi/2 + ICS = np.array([p_ee[0], p_ee[1], theta, 0.0, 0.0, 0.0]) + + elif sys_id == 'double_integrator': + ICS = np.array([p_ee[0], p_ee[1], 0.0, 0.0, 0.0]) + + elif sys_id == 'single_integrator': + ICS = np.array([p_ee[0], p_ee[1], 0.0]) + + return ICS, continue_flag + + def plot_policy(self, tau, x, y, steps, n_updates, diff_loc=0): + ''' Plot policy rollout from a single initial state as well as state and control trajectories ''' + timesteps = self.self.conf.dt*np.arange(steps) + + fig = plt.figure(figsize=(12,8)) + plt.suptitle('POLICY: Discrete model, N try = {} N updates = {}'.format(self.N_try,n_updates), y=1) + + ax1 = fig.add_subplot(2, 2, 1) + ax1.plot(timesteps, x, 'ro', linewidth=1, markersize=1, legedn='x') + ax1.plot(timesteps, y, 'bo', linewidth=1, markersize=1, legend='y') + ax1.set_xlabel('Time [s]') + ax1.set_ylabel('[m]') + ax1.set_title('End-Effector Position') + ax1.legend() + ax1.grid(True) + + col = ['ro', 'bo', 'go'] + ax2 = fig.add_subplot(2, 2, self.conf.nb_action) + for i in range(self.conf.nb_action): + ax2.plot(timesteps, tau[:,i], col[i], linewidth=1, markersize=1,legend='tau{}'.format(i)) + ax2.set_xlabel('Time [s]') + ax2.set_title('Controls') + ax2.legend() + ax2.grid(True) + + ax3 = fig.add_subplot(1, 2, 2) + ax3.plot(x, y, 'ro', linewidth=1, markersize=1) + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax3.add_artist(obs_plot_list[i]) + ax3.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=10) + ax3.set_xlim(self.xlim) + ax3.set_ylim(self.ylim) + ax3.set_aspect('equal', 'box') + ax3.set_xlabel('X [m]') + ax3.set_ylabel('Y [m]') + ax3.set_title('Plane') + ax3.grid(True) + + fig.tight_layout() + + if diff_loc==0: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationSingleInit_{}_{}'.format(self.N_try,n_updates)) + else: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationMultiInit_{}_{}'.format(self.N_try,n_updates)) + + plt.clf() + plt.close(fig) + + def plot_policy_eval(self, p_list, n_updates, diff_loc=0, theta=0): + ''' Plot only policy rollouts from multiple initial states ''' + fig = plt.figure(figsize=(12,8)) + plt.suptitle('POLICY: Discrete model, N try = {} N updates = {}'.format(self.N_try,n_updates), y=1) + + ax = fig.add_subplot(1, 1, 1) + for idx in range(len(p_list)): + ax.plot(p_list[idx][:,0], p_list[idx][:,1], marker='o', linewidth=0.3, markersize=1) + ax.plot(p_list[idx][0,0],p_list[idx][0,1],'ko',markersize=5) + if self.conf.system_id == 'car_park': + theta = p_list[idx][-1,2] + fancybox = FancyBboxPatch((0 - self.conf.L/2, 0 - self.conf.W/2), self.conf.L, self.conf.W, edgecolor='none', alpha=0.5, boxstyle='round,pad=0') + fancybox.set_transform(Affine2D().rotate_deg(np.rad2deg(theta)).translate(p_list[idx][-1,0], p_list[idx][-1,1]) + ax.transData) + ax.add_patch(fancybox) + + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + + ax.plot(self.conf.TARGET_STATE[0],self.conf.TARGET_STATE[1],'b*',markersize=10) + + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.grid(True) + fig.tight_layout() + if diff_loc==0: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationSingleInit_{}_{}'.format(self.N_try,n_updates)) + else: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationMultiInit_{}_{}'.format(self.N_try,n_updates)) + + plt.clf() + plt.close(fig) + + def rollout(self,update_step_cntr, actor_model, init_states_sim, diff_loc=0): + ''' Plot rollout of the actor from some initial states. It generates the results and then calls plot_policy() and plot_policy_eval() ''' + #tau_all_sim = [] + p_ee_all_sim = [] + + returns = {} + + for k in range(len(init_states_sim)): + rollout_controls = np.zeros((self.conf.NSTEPS,self.conf.nb_action)) + rollout_states = np.zeros((self.conf.NSTEPS+1,self.conf.nb_state)) + rollout_p_ee = np.zeros((self.conf.NSTEPS+1,3)) + rollout_episodic_reward = 0 + + rollout_p_ee[0,:] = self.env.get_end_effector_position(init_states_sim[k]) + rollout_states[0,:] = np.copy(init_states_sim[k]) + + for i in range(self.conf.NSTEPS): + rollout_controls[i,:] = tf.squeeze(self.NN.eval(actor_model, np.array([rollout_states[i,:]]))).numpy() + rollout_states[i+1,:], rwrd_sim = self.env.step(self.conf.cost_weights_running, rollout_states[i,:],rollout_controls[i,:]) + rollout_p_ee[i+1,:] = self.env.get_end_effector_position(rollout_states[i+1,:]) + + rollout_p_ee[i+1,-1] = rollout_states[i+1,2] ### !!! ### + + rollout_episodic_reward += rwrd_sim + + if k==0: + print("N try = {}: Simulation Return @ N updates = {} ==> {}".format(self.N_try,update_step_cntr,rollout_episodic_reward)) + + p_ee_all_sim.append(rollout_p_ee) + + returns[init_states_sim[k][0],init_states_sim[k][1]] = rollout_episodic_reward + + self.plot_policy_eval(p_ee_all_sim,update_step_cntr, diff_loc=diff_loc) + + return returns + + def plot_results(self, tau, ee_pos_TO, ee_pos_RL, steps, to=0): + ''' Plot results from TO and episode to check consistency ''' + timesteps = self.conf.dt*np.arange(steps+1) + fig = plt.figure(figsize=(12,8)) + if to: + plt.suptitle('TO EXPLORATION: N try = {}'.format(self.N_try), y=1, fontsize=20) + else: + plt.suptitle('POLICY EXPLORATION: N try = {}'.format(self.N_try), y=1, fontsize=20) + + ax1 = fig.add_subplot(2, 2, 1) + ax1.plot(timesteps, ee_pos_TO[:,0], 'ro', linewidth=1, markersize=1,legend="x_TO") + ax1.plot(timesteps, ee_pos_TO[:,1], 'bo', linewidth=1, markersize=1,legend="y_TO") + ax1.plot(timesteps, ee_pos_RL[:,0], 'go', linewidth=1, markersize=1,legend="x_RL") + ax1.plot(timesteps, ee_pos_RL[:,1], 'ko', linewidth=1, markersize=1,legend="y_RL") + ax1.set_xlabel('Time [s]') + ax1.set_ylabel('[m]') + ax1.set_title('End-Effector Position') + ax1.set_xlim(0, timesteps[-1]) + ax1.legend() + ax1.grid(True) + + ax2 = fig.add_subplot(2, 2, 3) + col = ['ro', 'bo', 'go'] + for i in range(self.conf.nb_action): + ax2.plot(timesteps[:-1], tau[:,i], col[i], linewidth=1, markersize=1,legend='tau{}'.format(i)) + ax2.set_xlabel('Time [s]') + ax2.set_title('Controls') + ax2.legend() + ax2.grid(True) + + ax3 = fig.add_subplot(1, 2, 2) + ax3.plot(ee_pos_TO[:,0], ee_pos_TO[:,1], 'ro', linewidth=1, markersize=2,legend='TO') + ax3.plot(ee_pos_RL[:,0], ee_pos_RL[:,1], 'bo', linewidth=1, markersize=2,legend='RL') + ax3.plot([ee_pos_TO[0,0]],[ee_pos_TO[0,1]],'ro',markersize=5) + ax3.plot([ee_pos_RL[0,0]],[ee_pos_RL[0,1]],'bo',markersize=5) + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax3.add_artist(obs_plot_list[i]) + ax3.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax3.set_xlim(self.xlim) + ax3.set_ylim(self.ylim) + ax3.set_aspect('equal', 'box') + ax3.set_xlabel('X [m]') + ax3.set_ylabel('Y [m]') + ax3.set_title('Plane') + ax3.legend() + ax3.grid(True) + + fig.tight_layout() + #plt.show() + + def plot_Return(self, ep_reward_list): + ''' Plot returns (not so meaningful given that the initial state, so also the time horizon, of each episode is randomized) ''' + fig = plt.figure(figsize=(15,8)) + ax = fig.add_subplot(1, 1, 1) + ax.set_yscale('log') + ax.plot(ep_reward_list**2) + ax.set_xlabel("Episode") + ax.set_ylabel("Return") + ax.set_title("N_try = {}".format(self.N_try)) + ax.grid(True) + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/EpReturn_{}'.format(self.N_try)) + plt.close() + + def plot_Critic_Value_function(self, critic_model, n_update, sys_id, name='V'): + ''' Plot Value function as learned by the critic ''' + if sys_id == 'manipulator': + N_discretization_x = 60 + 1 + N_discretization_y = 60 + 1 + + plot_data = np.zeros(N_discretization_y*N_discretization_x)*np.nan + ee_pos = np.zeros((N_discretization_y*N_discretization_x,3))*np.nan + + for k_x in range(N_discretization_x): + for k_y in range(N_discretization_y): + ICS = self.env.reset() + ICS[-1] = 0 + ee_pos[k_x*(N_discretization_y)+k_y,:] = self.env.get_end_effector_position(ICS) + plot_data[k_x*(N_discretization_y)+k_y] = self.NN.eval(critic_model, np.array([ICS])) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot() + plt.scatter(ee_pos[:,0], ee_pos[:,1], c=plot_data, cmap=cm.coolwarm, antialiased=False) + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + plt.colorbar() + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/{}_{}'.format(self.conf.Fig_path,self.N_try,name,int(n_update))) + plt.close() + + else: + N_discretization_x = 30 + 1 + N_discretization_y = 30 + 1 + + plot_data = np.zeros((N_discretization_y,N_discretization_x))*np.nan + + ee_x = np.linspace(-15, 15, N_discretization_x) + ee_y = np.linspace(-15, 15, N_discretization_y) + + for k_y in range(N_discretization_y): + for k_x in range(N_discretization_x): + p_ee = np.array([ee_x[k_x], ee_y[k_y], 0]) + ICS, continue_flag = self.compute_ICS(p_ee, sys_id, continue_flag=0) + if continue_flag: + continue + plot_data[k_x,k_y] = self.NN.eval(critic_model, np.array([ICS])) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot() + plt.contourf(ee_x, ee_y, plot_data.T, cmap=cm.coolwarm, antialiased=False) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + plt.colorbar() + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/{}_{}'.format(self.conf.Fig_path,self.N_try,name,int(n_update))) + plt.close() + + def plot_Critic_Value_function_from_sample(self, n_update, NSTEPS_SH, state_arr, reward_arr): + # Store transition after computing the (partial) cost-to go when using n-step TD (from 0 to Monte Carlo) + reward_to_go_arr = np.zeros(sum(NSTEPS_SH)+len(NSTEPS_SH)*1) + idx = 0 + for n in range(len(NSTEPS_SH)): + for i in range(NSTEPS_SH[n]+1): + # Compute the partial cost to go + reward_to_go_arr[idx] = sum(reward_arr[n][i:]) + idx += 1 + + state_arr = np.concatenate(state_arr, axis=0) + ee_pos_arr = np.zeros((len(state_arr),3)) + for i in range(state_arr.shape[0]): + ee_pos_arr[i,:] = self.env.get_end_effector_position(state_arr[i]) + + + mi = min(reward_to_go_arr) + ma = max(reward_to_go_arr) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot()#projection='3d') + norm = colors.Normalize(vmin=mi,vmax=ma) + + obs_plot_list = self.plot_obstaces(a=0.5) + + ax.scatter(ee_pos_arr[:,0],ee_pos_arr[:,1], c=reward_to_go_arr, norm=norm, cmap=cm.coolwarm, marker='x') + + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + + plt.colorbar(cm.ScalarMappable(norm=norm,cmap=cm.coolwarm)) + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/V_sample_{}'.format(self.conf.Fig_path,self.N_try,int(n_update))) + plt.close() + + def plot_ICS(self,state_arr): + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + for j in range(len(state_arr)): + ax.scatter(state_arr[j][0,0],state_arr[j][0,1]) + obs_plot_list = plot_fun.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.fig_ax_lim[0].tolist()) + ax.set_ylim(self.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/ICS_{}_S{}'.format(conf.Fig_path,N_try,update_step_counter,int(w_S))) + plt.close(fig) + + def plot_rollout_and_traj_from_ICS(self, init_state, n_update, actor_model, TrOp, tag, steps=200): + ''' Plot results from TO and episode to check consistency ''' + colors = cm.coolwarm(np.linspace(0.1,1,len(init_state))) + + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + + for j in range(len(init_state)): + + ee_pos_TO = np.zeros((steps,3)) + ee_pos_RL = np.zeros((steps,3)) + + RL_states = np.zeros((steps,self.conf.nb_state)) + RL_action = np.zeros((steps-1,self.conf.nb_action)) + RL_states[0,:] = init_state[j,:] + ee_pos_RL[0,:] = self.env.get_end_effector_position(RL_states[0,:]) + + for i in range(steps-1): + RL_action[i,:] = self.NN.eval(actor_model, np.array([RL_states[i,:]])) + RL_states[i+1,:] = self.env.simulate(RL_states[i,:], RL_action[i,:]) + ee_pos_RL[i+1,:] = self.env.get_end_effector_position(RL_states[i+1,:]) + + TO_states, _ = TrOp.TO_System_Solve3(init_state[j,:], RL_states.T, RL_action.T, steps-1) + + try: + for i in range(steps): + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[i,:]) + except: + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[0,:]) + + ax.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax.scatter(ee_pos_TO[0,0],ee_pos_TO[0,1],color=colors[j]) + ax.scatter(ee_pos_RL[0,0],ee_pos_RL[0,1],color=colors[j]) + ax.plot(ee_pos_TO[1:,0],ee_pos_TO[1:,1],color=colors[j]) + ax.plot(ee_pos_RL[1:,0],ee_pos_RL[1:,1],'--',color=colors[j]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.set_title('Plane') + #ax.legend() + ax.grid(True) + + plt.savefig('{}/N_try_{}/ee_traj_{}_{}'.format(self.conf.Fig_path,self.N_try,int(n_update), tag)) + + def plot_ICS(self, input_arr, cs=0): + if cs == 1: + p_arr = np.zeros((len(input_arr),3)) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + for j in range(len(input_arr)): + p_arr[j,:] = input_arr[j,:] + ax.scatter(p_arr[:,0],p_arr[:,1]) + obs_plot_list = self.plot_obstaces(a = 0.5) + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.conf.fig_ax_lim[0].tolist()) + ax.set_ylim(self.conf.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + ax.grid() + plt.savefig('{}/N_try_{}/ICS'.format(self.conf.Fig_path,self.N_try)) + plt.close(fig) + else: + p_arr = np.zeros((len(input_arr),3)) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + + for j in range(len(input_arr)): + p_arr[j,:] = self.env.get_end_effector_position(input_arr[j]) + ax.scatter(p_arr[:,0],p_arr[:,1]) + obs_plot_list = self.plot_obstaces(a = 0.5) + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.conf.fig_ax_lim[0].tolist()) + ax.set_ylim(self.conf.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + ax.grid() + plt.savefig('{}/N_try_{}/ICS'.format(self.conf.Fig_path,self.N_try)) + plt.close(fig) + + def plot_traj_from_ICS(self, init_state, TrOp, RLAC, update_step_counter=0,ep=0,steps=200, init=0,continue_flag=1): + ''' Plot results from TO and episode to check consistency ''' + colors = cm.coolwarm(np.linspace(0.1,1,len(init_state))) + + fig = plt.figure(figsize=(12,8)) + ax1 = fig.add_subplot(1,2,1) + ax2 = fig.add_subplot(1,2,2) + + for j in range(len(init_state)): + + ee_pos_TO = np.zeros((steps,3)) + ee_pos_RL = np.zeros((steps,3)) + + if init == 0: + # zeros + _, init_TO_states, init_TO_controls, _, success_init_flag = RLAC.create_TO_init(0, init_state[j,:]) + elif init == 1: + # NN + _, init_TO_states, init_TO_controls, _, success_init_flag = RLAC.create_TO_init(1, init_state[j,:]) + + if success_init_flag: + _, _, TO_states, _, _, _ = TrOp.TO_System_Solve(init_state[j,:], init_TO_states, init_TO_controls, steps-1) + else: + continue + + try: + for i in range(steps): + ee_pos_RL[i,:] = self.env.get_end_effector_position(init_TO_states[i,:]) + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[i,:]) + except: + ee_pos_RL[i,:] = self.env.get_end_effector_position(init_TO_states[0,:]) + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[0,:]) + + ax1.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax1.scatter(ee_pos_RL[0,0],ee_pos_RL[0,1],color=colors[j]) + ax1.plot(ee_pos_RL[1:,0],ee_pos_RL[1:,1],'--',color=colors[j]) + + ax2.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax2.scatter(ee_pos_TO[0,0],ee_pos_TO[0,1],color=colors[j]) + ax2.plot(ee_pos_TO[1:,0],ee_pos_TO[1:,1],color=colors[j]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax1.add_patch(obs_plot_list[i]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax2.add_patch(obs_plot_list[i]) + + ax1.set_xlim(self.xlim) + ax1.set_ylim(self.ylim) + ax1.set_aspect('equal', 'box') + ax1.set_xlabel('X [m]') + ax1.set_ylabel('Y [m]') + ax1.set_title('Warmstart traj.') + + ax2.set_xlim(self.xlim) + ax2.set_ylim(self.ylim) + ax2.set_aspect('equal', 'box') + ax2.set_xlabel('X [m]') + #ax2.set_ylabel('Y [m]') + ax2.set_title('TO traj.') + #ax.legend() + ax1.grid(True) + ax2.grid(True) + + plt.savefig('{}/N_try_{}/ee_traj_{}_{}'.format(self.conf.Fig_path,self.N_try,init,update_step_counter)) + + + + +if __name__ == '__main__': + import os + import sys + import time + import random + import importlib + import numpy as np + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # {'0' -> show all logs, '1' -> filter out info, '2' -> filter out warnings} + import tensorflow as tf + import matplotlib.pyplot as plt + import mpl_toolkits.mplot3d.art3d as art3d + + from RL import RL_AC + from plot_utils import PLOT + from NeuralNetwork import NN + + ### Input ### + N_try = 0 + + seed = 0 + tf.random.set_seed(seed) # Set tensorflow seed + random.seed(seed) # Set random seed + + system_id = 'car_park' + + TO_method = 'casadi' + + recover_training_flag = 0 + + CPU_flag = 0 + if CPU_flag: + os.environ["CUDA_VISIBLE_DEVICES"]="-1" + tf.config.experimental.list_physical_devices('GPU') + + nb_cpus = 1 + + w_S = 0 + ################################# + + # Import configuration file and environment file + system_map = { + 'single_integrator': ('conf_single_integrator', 'SingleIntegrator'), + 'double_integrator': ('conf_double_integrator', 'DoubleIntegrator'), + 'car': ('conf_car', 'Car'), + 'car_park': ('conf_car_park', 'CarPark'), + 'manipulator': ('conf_manipulator', 'Manipulator'), + 'ur5': ('conf_ur5', 'UR5') + } + + try: + conf_module, env_class = system_map[system_id] + conf = importlib.import_module(conf_module) + Environment = getattr(importlib.import_module('environment'), env_class) + except KeyError: + print('System {} not found'.format(system_id)) + sys.exit() + + + + # Create folders to store the results and the trained NNs and save configuration + for path in conf.path_list: + os.makedirs(path + '/N_try_{}'.format(N_try), exist_ok=True) + os.makedirs(conf.Config_path, exist_ok=True) + + params = [p for p in conf.__dict__ if not p.startswith("__")] + with open(conf.Config_path + '/config{}.txt'.format(N_try), 'w') as f: + for p in params: + f.write('{} = {}\n'.format(p, conf.__dict__[p])) + f.write('Seed = {}\n'.format(seed)) + f.write('w_S = {}'.format(w_S)) + + + + # Create environment instances + env = Environment(conf) + + # Create NN instance + NN_inst = NN(env, conf, w_S) + + # Create RL_AC instance + RLAC = RL_AC(env, NN_inst, conf, N_try) + + # Set initial weights of the NNs, initialize the counter of the updates and setup NN models + if recover_training_flag: + recover_training = np.array([conf.NNs_path_rec, conf.N_try_rec, conf.update_step_counter_rec]) + update_step_counter = conf.update_step_counter_rec + nb_starting_episode = (conf.update_step_counter_rec/conf.UPDATE_LOOPS)+1 + + RLAC.setup_model(recover_training) + else: + update_step_counter = 0 + nb_starting_episode = 0 + + RLAC.setup_model() + + # Create PLOT instance + plot_fun = PLOT(N_try, env, NN_inst, conf) + + plot_fun.plot_Reward() \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_0/replay_buffer.py b/Results Single Integrator/Results set test/Code/N_try_0/replay_buffer.py new file mode 100644 index 0000000..1cbf4b6 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/replay_buffer.py @@ -0,0 +1,240 @@ +import math +import random +import numpy as np +import tensorflow as tf +import segment_tree +#from stable_baselines.common.segment_tree import SumSegmentTree, MinSegmentTree + + +class ReplayBuffer(object): + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param BATCH_SIZE : (int) Size of the mini-batch + :param nb_state : (int) State size (robot state size + 1) + ''' + + self.conf = conf + self.storage_mat = np.zeros((conf.REPLAY_SIZE, conf.nb_state + 1 + conf.nb_state + conf.nb_state + 1 + 1)) + self.next_idx = 0 + self.full = 0 + self.exp_counter = np.zeros(conf.REPLAY_SIZE) + + def add(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Add transitions to the buffer ''' + data = self.concatenate_sample(obses_t, rewards, obses_t1, dVdxs, dones, terms) + + if len(data) + self.next_idx > self.conf.REPLAY_SIZE: + self.storage_mat[self.next_idx:,:] = data[:self.conf.REPLAY_SIZE-self.next_idx,:] + self.storage_mat[:self.next_idx+len(data)-self.conf.REPLAY_SIZE,:] = data[self.conf.REPLAY_SIZE-self.next_idx:,:] + self.full = 1 + else: + self.storage_mat[self.next_idx:self.next_idx+len(data),:] = data + + self.next_idx = (self.next_idx + len(data)) % self.conf.REPLAY_SIZE + + def sample(self): + ''' Sample a batch of transitions ''' + # Select indexes of the batch elements + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + idxes = np.random.randint(0, max_idx, size=self.conf.BATCH_SIZE) + + obses_t = self.storage_mat[idxes, :self.conf.nb_state] + rewards = self.storage_mat[idxes, self.conf.nb_state:self.conf.nb_state+1] + obses_t1 = self.storage_mat[idxes, self.conf.nb_state+1:self.conf.nb_state*2+1] + dVdxs = self.storage_mat[idxes, self.conf.nb_state*2+1:self.conf.nb_state*3+1] + dones = self.storage_mat[idxes, self.conf.nb_state*3+1:self.conf.nb_state*3+2] + terms = self.storage_mat[idxes, self.conf.nb_state*3+2:self.conf.nb_state*3+3] + + # Priorities not used + weights = np.ones((self.conf.BATCH_SIZE,1)) + batch_idxes = None + + # Convert the sample in tensor + obses_t, rewards, obses_t1, dVdxs, dones, weights = self.convert_sample_to_tensor(obses_t, rewards, obses_t1, dVdxs, dones, weights) + + return obses_t, rewards, obses_t1, dVdxs, dones, terms, weights, batch_idxes + + def concatenate_sample(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Convert batch of transitions into a tensor ''' + obses_t = np.concatenate(obses_t, axis=0) + rewards = np.concatenate(rewards, axis=0) + obses_t1 = np.concatenate(obses_t1, axis=0) + dVdxs = np.concatenate(dVdxs, axis=0) + dones = np.concatenate(dones, axis=0) + terms = np.concatenate(terms, axis=0) + + return np.concatenate((obses_t, rewards.reshape(-1,1), obses_t1, dVdxs, dones.reshape(-1,1), terms.reshape(-1,1)),axis=1) + + def convert_sample_to_tensor(self, obses_t, rewards, obses_t1, dVdxs, dones, weights): + ''' Convert batch of transitions into a tensor ''' + obses_t = tf.convert_to_tensor(obses_t, dtype=tf.float32) + rewards = tf.convert_to_tensor(rewards, dtype=tf.float32) + obses_t1 = tf.convert_to_tensor(obses_t1, dtype=tf.float32) + dVdxs = tf.convert_to_tensor(dVdxs, dtype=tf.float32) + dones = tf.convert_to_tensor(dones, dtype=tf.float32) + weights = tf.convert_to_tensor(weights, dtype=tf.float32) + + return obses_t, rewards, obses_t1, dVdxs, dones, weights + + + +class PrioritizedReplayBuffer: + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param BATCH_SIZE : (int) Size of the mini-batch + :param nb_state : (int) State size (robot state size + 1) + :param prioritized_replay_alpha : (float) Determines how much prioritization is used, set to 0 to use a normal buffer + :param prioritized_replay_beta : (float) Small positive constant that prevents the edge-case of transitions not being revisited once their error is zero + ''' + + self.conf = conf + + self.storage_mat = np.zeros((conf.REPLAY_SIZE, conf.nb_state + 1 + conf.nb_state + conf.nb_state + 1 + 1)) + self.full = 0 + self.next_idx = 0 + self.exp_counter = np.zeros(self.conf.REPLAY_SIZE) + self.priorities = np.empty(self.conf.REPLAY_SIZE) + + assert conf.prioritized_replay_alpha >= 0 + assert conf.prioritized_replay_beta > 0 + + it_capacity = 1 + while it_capacity < self.conf.REPLAY_SIZE: + it_capacity *= 2 + + self._it_sum = SumSegmentTree(it_capacity) + self._it_min = MinSegmentTree(it_capacity) + self._max_priority = 1.0 + + #self.RB_type = 'ReLO' + + self.MSE = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE) + + def add(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Add transitions to the buffer ''' + data = self.concatenate_sample(obses_t, rewards, obses_t1, dVdxs, dones, terms) + + if len(data) + self.next_idx > self.conf.REPLAY_SIZE: + self.storage_mat[self.next_idx:,:] = data[:self.conf.REPLAY_SIZE-self.next_idx,:] + self.storage_mat[:self.next_idx+len(data)-self.conf.REPLAY_SIZE,:] = data[self.conf.REPLAY_SIZE-self.next_idx:,:] + self.full = 1 + else: + self.storage_mat[self.next_idx:self.next_idx+len(data),:] = data + + for i in range(len(data)): + self._it_sum[(self.next_idx+i) % self.conf.REPLAY_SIZE] = self._max_priority ** self.conf.prioritized_replay_alpha + self._it_min[(self.next_idx+i) % self.conf.REPLAY_SIZE] = self._max_priority ** self.conf.prioritized_replay_alpha + + self.next_idx = (self.next_idx + len(data)) % self.conf.REPLAY_SIZE + + def _sample_proportional(self): + ''' Sample a batch of transitions ''' + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + + idx_arr = np.zeros(self.conf.BATCH_SIZE) + + p_total = self._it_sum.sum(0, max_idx - 1) + + segment = p_total / self.conf.BATCH_SIZE + + for i in range(self.conf.BATCH_SIZE): + p = random.random() * segment + i * segment #random.random(segment*i, segment*(i+1)) + idx = self._it_sum.find_prefixsum_idx(p) + idx_arr[i] = idx + + return idx_arr + + def sample(self): + ''' Sample a batch of transitions ''' + # Select indexes of the batch elements + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + + idxes = self._sample_proportional() + batch_idxes = idxes.astype(int) + + # Compute weights normalization + p_min = self._it_min.min() / self._it_sum.sum() + max_weight = (p_min * max_idx) ** (-self.conf.prioritized_replay_beta) + + self.exp_counter[batch_idxes] += 1 + self.priorities[batch_idxes] = self._it_sum[batch_idxes] / self._it_sum.sum() + weights = (self.priorities[batch_idxes] * max_idx) ** (-self.conf.prioritized_replay_beta) / max_weight + + obses_t = self.storage_mat[batch_idxes, :self.conf.nb_state] + rewards = self.storage_mat[batch_idxes, self.conf.nb_state:self.conf.nb_state+1] + obses_t1 = self.storage_mat[batch_idxes, self.conf.nb_state+1:self.conf.nb_state*2+1] + dVdxs = self.storage_mat[batch_idxes, self.conf.nb_state*2+1:self.conf.nb_state*3+1] + dones = self.storage_mat[batch_idxes, self.conf.nb_state*3+1:self.conf.nb_state*3+2] + terms = self.storage_mat[batch_idxes, self.conf.nb_state*3+2:self.conf.nb_state*3+3] + + # Convert the sample in tensor + obses_t, rewards, obses_t1, dVdxs, dones, weights = self.convert_sample_to_tensor(obses_t, rewards, obses_t1, dVdxs, dones, weights) + + return obses_t, rewards, obses_t1, dVdxs, dones, terms, weights, batch_idxes + + def update_priorities(self, idxes, reward_to_go_batch, critic_value, target_critic_value=None): + '''Update priorities of sampled transitions ''' + # Create td_errors + if self.RB_type == 'ReLO': + # MSE(Delta V_c) - MSE(Delta V_t) + td_errors = self.MSE(reward_to_go_batch, critic_value).numpy()-self.MSE(reward_to_go_batch, target_critic_value).numpy() + td_errors_norm = np.clip(td_errors, 0, np.max(td_errors)) + else: # 'PER' is default self.RB_type + # |TD_error_i| + td_errors_norm = tf.math.abs(tf.math.subtract(reward_to_go_batch, critic_value)) + td_errors_norm = td_errors_norm[:,0] + + # Compute the freshness discount factor + fresh_disc_factor = self.conf.fresh_factor**self.exp_counter[idxes] + + # Compute new priorities: p_i = mu**C_i * td_error + self.conf.prioritized_replay_eps + new_priorities = fresh_disc_factor * td_errors_norm + self.conf.prioritized_replay_eps + + # Sets priority of transition at index idxes[i] in buffer to priorities[i] + assert len(idxes) == len(new_priorities) + for idx, priority in zip(idxes, new_priorities): + assert priority > 0 + + idx = int(idx) + + self._it_sum[idx] = priority ** self.conf.prioritized_replay_alpha + self._it_min[idx] = priority ** self.conf.prioritized_replay_alpha + + self._max_priority = max(self._max_priority, priority) + + def concatenate_sample(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Convert batch of transitions into a tensor ''' + obses_t = np.concatenate(obses_t, axis=0) + rewards = np.concatenate(rewards, axis=0) + obses_t1 = np.concatenate(obses_t1, axis=0) + dVdxs = np.concatenate(dVdxs, axis=0) + dones = np.concatenate(dones, axis=0) + terms = np.concatenate(terms, axis=0) + + return np.concatenate((obses_t, rewards.reshape(-1,1), obses_t1, dVdxs, dones.reshape(-1,1), terms.reshape(-1,1)),axis=1) + + def convert_sample_to_tensor(self, obses_t, rewards, obses_t1, dVdxs, dones, weights): + ''' Convert batch of transitions into a tensor ''' + obses_t = tf.convert_to_tensor(obses_t, dtype=tf.float32) + rewards = tf.convert_to_tensor(rewards, dtype=tf.float32) + obses_t1 = tf.convert_to_tensor(obses_t1, dtype=tf.float32) + dVdxs = tf.convert_to_tensor(dVdxs, dtype=tf.float32) + dones = tf.convert_to_tensor(dones, dtype=tf.float32) + weights = tf.convert_to_tensor(weights, dtype=tf.float32) + + return obses_t, rewards, obses_t1, dVdxs, dones, weights \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_0/robot_utils.py b/Results Single Integrator/Results set test/Code/N_try_0/robot_utils.py new file mode 100644 index 0000000..d3cc937 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/robot_utils.py @@ -0,0 +1,637 @@ +import os +import math +import random +#import gepetto#.corbaserver +import time +import subprocess +import numpy as np +import pinocchio as pin +from numpy.linalg import norm +from pinocchio.robot_wrapper import RobotWrapper as PinocchioRobotWrapper + +class Contact: + ''' A contact between a contact-point and a contact-surface + ''' + def __init__(self, contact_point, contact_surface): + self.cp = contact_point + self.cs = contact_surface + self.reset_contact_position() + + def reset_contact_position(self): + # Initialize anchor point p0, that is the initial (0-load) position of the spring + self.p0 = self.cp.get_position() + self.in_contact = True + + def compute_force(self): + self.f, self.p0 = self.cs.compute_force(self.cp, self.p0) + return self.f + + def get_jacobian(self): + return self.cp.get_jacobian() + +class RobotWrapper(PinocchioRobotWrapper): + + @staticmethod + def BuildFromURDF(filename, package_dirs=None, root_joint=None, verbose=False, meshLoader=None): + robot = RobotWrapper() + robot.initFromURDF(filename, package_dirs, root_joint, verbose, meshLoader) + return robot + + @property + def na(self): + if(self.model.joints[0].nq==7): + return self.model.nv-6 + return self.model.nv + + def mass(self, q, update=True): + if(update): + return pin.crba(self.model, self.data, q) + return self.data.M + + def nle(self, q, v, update=True): + if(update): + return pin.nonLinearEffects(self.model, self.data, q, v) + return self.data.nle + + def com(self, q=None, v=None, a=None, update=True): + if(update==False or q is None): + return PinocchioRobotWrapper.com(self, q) + if a is None: + if v is None: + return PinocchioRobotWrapper.com(self, q) + return PinocchioRobotWrapper.com(self, q, v) + return PinocchioRobotWrapper.com(self, q, v,a) + + def Jcom(self, q, update=True): + if(update): + return pin.jacobianCenterOfMass(self.model, self.data, q) + return self.data.Jcom + + def momentumJacobian(self, q, v, update=True): + if(update): + pin.ccrba(self.model, self.data, q, v) + return self.data.Ag + + def computeAllTerms(self, q, v): + ''' pin.computeAllTerms is equivalent to calling: + pinocchio::forwardKinematics + pinocchio::crba + pinocchio::nonLinearEffects + pinocchio::computeJointJacobians + pinocchio::centerOfMass + pinocchio::jacobianCenterOfMass + pinocchio::kineticEnergy + pinocchio::potentialEnergy + This is too much for our needs, so we call only the functions + we need, including those for the frame kinematics + ''' + #pin.computeAllTerms(self.model, self.data, q, v); + pin.forwardKinematics(self.model, self.data, q, v, np.zeros(self.model.nv)) + pin.computeJointJacobians(self.model, self.data) + pin.updateFramePlacements(self.model, self.data) + pin.crba(self.model, self.data, q) + pin.nonLinearEffects(self.model, self.data, q, v) + + def forwardKinematics(self, q, v=None, a=None): + if v is not None: + if a is not None: + pin.forwardKinematics(self.model, self.data, q, v, a) + else: + pin.forwardKinematics(self.model, self.data, q, v) + else: + pin.forwardKinematics(self.model, self.data, q) + + def inverseKinematics(self, ee_coords): + ''' Inverse kinematics algorithm to compute a joint configuration given the EE coordinates''' + + oMdes = pin.SE3(np.eye(3), np.array([ee_coords[0], ee_coords[1], 0.0])) + random.seed(123) + for j in range(100): + q = np.array([random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi)]) + # q = pin.neutral(self.model) + + pin.forwardKinematics(self.model, self.data, q) + pin.updateFramePlacements(self.model, self.data) + eps = 1e-4 + IT_MAX = 1000 + DT = 1e-1 + damp = 1e-12 + frame_id = self.model.getFrameId('fixed_ee') + i=0 + while True: + pin.forwardKinematics(self.model,self.data,q) + pin.updateFramePlacements(self.model, self.data) + dMi = oMdes.actInv(self.data.oMf[frame_id]) + err = pin.log(dMi).vector + if norm(err) < eps: + success = True + break + if i >= IT_MAX: + success = False + break + J = pin.computeFrameJacobian(self.model,self.data,q,frame_id) + v = - J.T.dot(np.linalg.solve(J.dot(J.T) + damp * np.eye(6), err)) + q = pin.integrate(self.model,q,v*DT) + if not i % 10: + print('%d: error = %s' % (i, err.T)) + i += 1 + + if success: + print("Convergence achieved!") + break + else: + print("\nWarning: the iterative algorithm has not reached convergence to the desired precision. Retry ({}) with another initial configuration".format(j)) + + return q, success + + def frameJacobian(self, q, index, update=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + ''' Call computeFrameJacobian if update is true. If not, user should call computeFrameJacobian first. + Then call getFrameJacobian and return the Jacobian matrix. + ref_frame can be: ReferenceFrame.LOCAL, ReferenceFrame.WORLD, ReferenceFrame.LOCAL_WORLD_ALIGNED + ''' + if(update): + pin.computeFrameJacobian(self.model, self.data, q, index) + return pin.getFrameJacobian(self.model, self.data, index, ref_frame) + + def frameVelocity(self, q, v, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v) + v_local = pin.getFrameVelocity(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return v_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + v_world = H.act(v_local) + return v_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + v = Hr.act(v_local) + return v + + def frameAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def frameClassicAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + v = pin.getFrameVelocity(self.model, self.data, index) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + a_local.linear += np.cross(v.angular, v.linear, axis=0) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def deactivateCollisionPairs(self, collision_pair_indexes): + for i in collision_pair_indexes: + self.collision_data.deactivateCollisionPair(i) + + def addAllCollisionPairs(self): + self.collision_model.addAllCollisionPairs() + self.collision_data = pin.GeometryData(self.collision_model) + + def isInCollision(self, q, stop_at_first_collision=True): + return pin.computeCollisions(self.model, self.data, self.collision_model, self.collision_data, np.asmatrix(q).reshape((self.model.nq,1)), stop_at_first_collision) + + def findFirstCollisionPair(self, consider_only_active_collision_pairs=True): + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + return (i, self.collision_model.collisionPairs[i]) + return None + + def findAllCollisionPairs(self, consider_only_active_collision_pairs=True): + res = [] + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + res += [(i, self.collision_model.collisionPairs[i])] + return res + +class RobotSimulator: + + def __init__(self, robot, q0_init, v0_init, simulation_type, tau_coulomb_max, use_viewer=False, DISPLAY_T=None, CAMERA_TRANSFORM=None, show_floor=False): + self.robot = robot + self.model = self.robot.model + self.data = self.model.createData() + self.t = 0.0 # time + self.nv = nv = self.model.nv # Dimension of joint velocities vector + self.na = na = robot.na # number of actuated joints + self.S = np.hstack((np.zeros((na, nv-na)), np.eye(na, na))) # Matrix S used as filter of vector of inputs U + + self.contacts = [] + self.candidate_contact_points = [] # candidate contact points + self.contact_surfaces = [] + + self.DISPLAY_T = DISPLAY_T # refresh period for viewer + self.use_viewer = use_viewer + self.tau_coulomb_max = tau_coulomb_max + self.display_counter = self.DISPLAY_T + self.init(q0_init, v0_init, True) + + self.tau_c = np.zeros(na) # Coulomb friction torque + self.simulation_type = simulation_type + self.set_coulomb_friction(tau_coulomb_max) + + # for gepetto viewer + self.gui = None + if(use_viewer): + try: + prompt = subprocess.getstatusoutput("ps aux |grep 'gepetto-gui'|grep -v 'grep'|wc -l") + if int(prompt[1]) == 0: + os.system('gepetto-gui &') + time.sleep(1) + except: + pass + gepetto.corbaserver.Client() + gepetto.Client() + self.robot.initViewer(loadModel=True) + self.gui = self.robot.viewer.gui + if(show_floor): + self.robot.viewer.gui.createSceneWithFloor('world') + self.gui.setLightingMode('world/floor', 'OFF') + self.robot.displayCollisions(False) + self.robot.displayVisuals(True) + self.robot.display(self.q) + try: + self.gui.setCameraTransform("python-pinocchio", CAMERA_TRANSFORM) + except: + self.gui.setCameraTransform(0, CAMERA_TRANSFORM) + + # Re-initialize the simulator + def init(self, q0=None, v0=None, reset_contact_positions=False): + self.first_iter = True + + if q0 is not None: + self.q = q0.copy() + + if(v0 is None): + self.v = np.zeros(self.robot.nv) + else: + self.v = v0.copy() + self.dv = np.zeros(self.robot.nv) + self.resize_contact_data(reset_contact_positions) + + def resize_contact_data(self, reset_contact_positions=False): + self.nc = len(self.contacts) # number of contacts + self.nk = 3*self.nc # size of contact force vector + self.f = np.zeros(self.nk) # contact forces + self.Jc = np.zeros((self.nk, self.model.nv)) # contact Jacobian + + # reset contact position + if(reset_contact_positions): + pin.forwardKinematics(self.model, self.data, self.q) + pin.updateFramePlacements(self.model, self.data) + for c in self.contacts: + c.reset_contact_position() + + self.compute_forces(compute_data=True) + + def set_coulomb_friction(self, tau_max): + self.tau_coulomb_max = 1e-2*tau_max*self.model.effortLimit + self.simulate_coulomb_friction = (norm(self.tau_coulomb_max)!=0.0) + + def collision_detection(self): + for s in self.contact_surfaces: # for each contact surface + for cp in self.candidate_contact_points: # for each candidate contact point + p = cp.get_position() + if(s.check_collision(p)): # check whether the point is colliding with the surface + if(not cp.active): # if the contact was not already active + print("Collision detected between point", cp.frame_name, " at ", p) + cp.active = True + cp.contact = Contact(cp, s) + self.contacts += [cp.contact] + self.resize_contact_data() + else: + if(cp.active): + print("Contact lost between point", cp.frame_name, " at ", p) + cp.active = False + self.contacts.remove(cp.contact) + self.resize_contact_data() + + def compute_forces(self, compute_data=True): + '''Compute the contact forces from q, v and elastic model''' + if compute_data: + pin.forwardKinematics(self.model, self.data, self.q, self.v) + # Computes the placements of all the operational frames according to the current joint placement stored in data + pin.updateFramePlacements(self.model, self.data) + self.collision_detection() + + i = 0 + for c in self.contacts: + self.f[i:i+3] = c.compute_force() + self.Jc[i:i+3, :] = c.get_jacobian() + i += 3 + return self.f + + def step(self, u, dt=None): + if dt is None: + dt = self.dt + + # (Forces are directly in the world frame, and aba wants them in the end effector frame) + pin.computeAllTerms(self.model, self.data, self.q, self.v) + pin.updateFramePlacements(self.model, self.data) + M = self.data.M + h = self.data.nle + self.collision_detection() + self.compute_forces(False) + + if(self.simulate_coulomb_friction and self.simulation_type=='timestepping'): + # minimize kinetic energy using time stepping + from quadprog import solve_qp + ''' + Solve a strictly convex quadratic program + + Minimize 1/2 x^T G x - a^T x + Subject to C.T x >= b + + Input Parameters + ---------- + G : array, shape=(n, n) + a : array, shape=(n,) + C : array, shape=(n, m) matrix defining the constraints + b : array, shape=(m), default=None, vector defining the constraints + meq : int, default=0 + the first meq constraints are treated as equality constraints, + all further as inequality constraints + ''' + # M (v' - v) = dt*S^T*(tau - tau_c) - dt*h + dt*J^T*f + # M v' = M*v + dt*(S^T*tau - h + J^T*f) - dt*S^T*tau_c + # M v' = b + B*tau_c + # v' = Minv*(b + B*tau_c) + b = M.dot(self.v) + dt*(self.S.T.dot(u) - h + self.Jc.T.dot(self.f)) + B = - dt*self.S.T + # Minimize kinetic energy: + # min v'.T * M * v' + # min (b+B*tau_c​).T*Minv*(b+B*tau_c​) + # min tau_c.T * B.T*Minv*B* tau_C + 2*b.T*Minv*B*tau_c + Minv = np.linalg.inv(M) + G = B.T.dot(Minv.dot(B)) + a = -b.T.dot(Minv.dot(B)) + C = np.vstack((np.eye(self.na), -np.eye(self.na))) + c = np.concatenate((-self.tau_coulomb_max, -self.tau_coulomb_max)) + solution = solve_qp(G, a, C.T, c, 0) + self.tau_c = solution[0] + self.v = Minv.dot(b + B.dot(self.tau_c)) + self.q = pin.integrate(self.model, self.q, self.v*dt) + self.dv = np.linalg.solve(M, self.S.T.dot(u-self.tau_c) - h + self.Jc.T.dot(self.f)) + elif(self.simulation_type=='euler' or self.simulate_coulomb_friction==False): + self.tau_c = self.tau_coulomb_max*np.sign(self.v[-self.na:]) + self.dv = np.linalg.solve(M, self.S.T.dot(u-self.tau_c) - h + self.Jc.T.dot(self.f)) + # v_mean = np.copy(self.v) + 0.5*dt*self.dv + self.q = pin.integrate(self.model, self.q, self.v*dt) + # self.q += dt*v_mean + self.v += self.dv*dt + else: + print("[ERROR] Unknown simulation type:", self.simulation_type) + + self.t += dt + return self.q, self.v, self.dv + + def reset(self): + self.first_iter = True + + def simulate(self, state, u, dt=0.05, ndt=1): + ''' Perform ndt steps, each lasting dt/ndt seconds ''' + self.q = state[:self.robot.nq] + self.v = state[self.robot.nq:self.robot.nv+self.robot.nq] + + tau_c_avg = 0*self.tau_c + for i in range(ndt): + self.q, self.v, self.dv = self.step(u, dt/ndt) + tau_c_avg += self.tau_c + self.tau_c = tau_c_avg / ndt + + if (self.use_viewer): + self.display_counter -= dt + if self.display_counter <= 0.0: + self.robot.display(self.q) + self.display_counter = self.DISPLAY_T + + return self.q, self.v, self.f + + def display(self, q): + if(self.use_viewer): + self.robot.display(q) + + +class RobotWrapper(PinocchioRobotWrapper): + + @staticmethod + def BuildFromURDF(filename, package_dirs=None, root_joint=None, verbose=False, meshLoader=None): + robot = RobotWrapper() + robot.initFromURDF(filename, package_dirs, root_joint, verbose, meshLoader) + return robot + + @property + def na(self): + if(self.model.joints[0].nq==7): + return self.model.nv-6 + return self.model.nv + + def mass(self, q, update=True): + if(update): + return pin.crba(self.model, self.data, q) + return self.data.M + + def nle(self, q, v, update=True): + if(update): + return pin.nonLinearEffects(self.model, self.data, q, v) + return self.data.nle + + def com(self, q=None, v=None, a=None, update=True): + if(update==False or q is None): + return PinocchioRobotWrapper.com(self, q) + if a is None: + if v is None: + return PinocchioRobotWrapper.com(self, q) + return PinocchioRobotWrapper.com(self, q, v) + return PinocchioRobotWrapper.com(self, q, v,a) + + def Jcom(self, q, update=True): + if(update): + return pin.jacobianCenterOfMass(self.model, self.data, q) + return self.data.Jcom + + def momentumJacobian(self, q, v, update=True): + if(update): + pin.ccrba(self.model, self.data, q, v) + return self.data.Ag + + def computeAllTerms(self, q, v): + ''' pin.computeAllTerms is equivalent to calling: + pinocchio::forwardKinematics + pinocchio::crba + pinocchio::nonLinearEffects + pinocchio::computeJointJacobians + pinocchio::centerOfMass + pinocchio::jacobianCenterOfMass + pinocchio::kineticEnergy + pinocchio::potentialEnergy + This is too much for our needs, so we call only the functions + we need, including those for the frame kinematics + ''' + #pin.computeAllTerms(self.model, self.data, q, v); + pin.forwardKinematics(self.model, self.data, q, v, np.zeros(self.model.nv)) + pin.computeJointJacobians(self.model, self.data) + pin.updateFramePlacements(self.model, self.data) + pin.crba(self.model, self.data, q) + pin.nonLinearEffects(self.model, self.data, q, v) + + def forwardKinematics(self, q, v=None, a=None): + if v is not None: + if a is not None: + pin.forwardKinematics(self.model, self.data, q, v, a) + else: + pin.forwardKinematics(self.model, self.data, q, v) + else: + pin.forwardKinematics(self.model, self.data, q) + + def inverseKinematics(self, ee_coords): + ''' Inverse kinematics algorithm to compute a joint configuration given the EE coordinates''' + + oMdes = pin.SE3(np.eye(3), np.array([ee_coords[0], ee_coords[1], 0.0])) + random.seed(123) + for j in range(100): + q = np.array([random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi)]) + # q = pin.neutral(self.model) + + pin.forwardKinematics(self.model, self.data, q) + pin.updateFramePlacements(self.model, self.data) + eps = 1e-4 + IT_MAX = 1000 + DT = 1e-1 + damp = 1e-12 + frame_id = self.model.getFrameId('fixed_ee') + i=0 + while True: + pin.forwardKinematics(self.model,self.data,q) + pin.updateFramePlacements(self.model, self.data) + dMi = oMdes.actInv(self.data.oMf[frame_id]) + err = pin.log(dMi).vector + if norm(err) < eps: + success = True + break + if i >= IT_MAX: + success = False + break + J = pin.computeFrameJacobian(self.model,self.data,q,frame_id) + v = - J.T.dot(np.linalg.solve(J.dot(J.T) + damp * np.eye(6), err)) + q = pin.integrate(self.model,q,v*DT) + if not i % 10: + print('%d: error = %s' % (i, err.T)) + i += 1 + + if success: + print("Convergence achieved!") + break + else: + print("\nWarning: the iterative algorithm has not reached convergence to the desired precision. Retry ({}) with another initial configuration".format(j)) + + return q, success + + def frameJacobian(self, q, index, update=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + ''' Call computeFrameJacobian if update is true. If not, user should call computeFrameJacobian first. + Then call getFrameJacobian and return the Jacobian matrix. + ref_frame can be: ReferenceFrame.LOCAL, ReferenceFrame.WORLD, ReferenceFrame.LOCAL_WORLD_ALIGNED + ''' + if(update): + pin.computeFrameJacobian(self.model, self.data, q, index) + return pin.getFrameJacobian(self.model, self.data, index, ref_frame) + + def frameVelocity(self, q, v, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v) + v_local = pin.getFrameVelocity(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return v_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + v_world = H.act(v_local) + return v_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + v = Hr.act(v_local) + return v + + def frameAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def frameClassicAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + v = pin.getFrameVelocity(self.model, self.data, index) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + a_local.linear += np.cross(v.angular, v.linear, axis=0) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def deactivateCollisionPairs(self, collision_pair_indexes): + for i in collision_pair_indexes: + self.collision_data.deactivateCollisionPair(i) + + def addAllCollisionPairs(self): + self.collision_model.addAllCollisionPairs() + self.collision_data = pin.GeometryData(self.collision_model) + + def isInCollision(self, q, stop_at_first_collision=True): + return pin.computeCollisions(self.model, self.data, self.collision_model, self.collision_data, np.asmatrix(q).reshape((self.model.nq,1)), stop_at_first_collision) + + def findFirstCollisionPair(self, consider_only_active_collision_pairs=True): + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + return (i, self.collision_model.collisionPairs[i]) + return None + + def findAllCollisionPairs(self, consider_only_active_collision_pairs=True): + res = [] + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + res += [(i, self.collision_model.collisionPairs[i])] + return res \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_0/segment_tree.py b/Results Single Integrator/Results set test/Code/N_try_0/segment_tree.py new file mode 100644 index 0000000..cb386ec --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/segment_tree.py @@ -0,0 +1,145 @@ +import operator + + +class SegmentTree(object): + def __init__(self, capacity, operation, neutral_element): + """Build a Segment Tree data structure. + + https://en.wikipedia.org/wiki/Segment_tree + + Can be used as regular array, but with two + important differences: + + a) setting item's value is slightly slower. + It is O(lg capacity) instead of O(1). + b) user has access to an efficient ( O(log segment size) ) + `reduce` operation which reduces `operation` over + a contiguous subsequence of items in the array. + + Paramters + --------- + capacity: int + Total size of the array - must be a power of two. + operation: lambda obj, obj -> obj + and operation for combining elements (eg. sum, max) + must form a mathematical group together with the set of + possible values for array elements (i.e. be associative) + neutral_element: obj + neutral element for the operation above. eg. float('-inf') + for max and 0 for sum. + """ + assert capacity > 0 and capacity & (capacity - 1) == 0, "capacity must be positive and a power of 2." + self._capacity = capacity + self._value = [neutral_element for _ in range(2 * capacity)] + self._operation = operation + + def _reduce_helper(self, start, end, node, node_start, node_end): + if start == node_start and end == node_end: + return self._value[node] + mid = (node_start + node_end) // 2 + if end <= mid: + return self._reduce_helper(start, end, 2 * node, node_start, mid) + else: + if mid + 1 <= start: + return self._reduce_helper(start, end, 2 * node + 1, mid + 1, node_end) + else: + return self._operation( + self._reduce_helper(start, mid, 2 * node, node_start, mid), + self._reduce_helper(mid + 1, end, 2 * node + 1, mid + 1, node_end) + ) + + def reduce(self, start=0, end=None): + """Returns result of applying `self.operation` + to a contiguous subsequence of the array. + + self.operation(arr[start], operation(arr[start+1], operation(... arr[end]))) + + Parameters + ---------- + start: int + beginning of the subsequence + end: int + end of the subsequences + + Returns + ------- + reduced: obj + result of reducing self.operation over the specified range of array elements. + """ + if end is None: + end = self._capacity + if end < 0: + end += self._capacity + end -= 1 + return self._reduce_helper(start, end, 1, 0, self._capacity - 1) + + def __setitem__(self, idx, val): + # index of the leaf + idx += self._capacity + self._value[idx] = val + idx //= 2 + while idx >= 1: + self._value[idx] = self._operation( + self._value[2 * idx], + self._value[2 * idx + 1] + ) + idx //= 2 + + def __getitem__(self, idx): + assert 0 <= idx < self._capacity + return self._value[self._capacity + idx] + + +class SumSegmentTree(SegmentTree): + def __init__(self, capacity): + super(SumSegmentTree, self).__init__( + capacity=capacity, + operation=operator.add, + neutral_element=0.0 + ) + + def sum(self, start=0, end=None): + """Returns arr[start] + ... + arr[end]""" + return super(SumSegmentTree, self).reduce(start, end) + + def find_prefixsum_idx(self, prefixsum): + """Find the highest index `i` in the array such that + sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum + + if array values are probabilities, this function + allows to sample indexes according to the discrete + probability efficiently. + + Parameters + ---------- + perfixsum: float + upperbound on the sum of array prefix + + Returns + ------- + idx: int + highest index satisfying the prefixsum constraint + """ + assert 0 <= prefixsum <= self.sum() + 1e-5 + idx = 1 + while idx < self._capacity: # while non-leaf + if self._value[2 * idx] > prefixsum: + idx = 2 * idx + else: + prefixsum -= self._value[2 * idx] + idx = 2 * idx + 1 + return idx - self._capacity + + +class MinSegmentTree(SegmentTree): + def __init__(self, capacity): + super(MinSegmentTree, self).__init__( + capacity=capacity, + operation=min, + neutral_element=float('inf') + ) + + def min(self, start=0, end=None): + """Returns min(arr[start], ..., arr[end])""" + + return super(MinSegmentTree, self).reduce(start, end) diff --git a/Results Single Integrator/Results set test/Code/N_try_0/utils.py b/Results Single Integrator/Results set test/Code/N_try_0/utils.py new file mode 100644 index 0000000..c426b89 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_0/utils.py @@ -0,0 +1,42 @@ +import tensorflow as tf +import numpy as np + +def array2tensor(array): + + return tf.expand_dims(tf.convert_to_tensor(array), 0) + +def de_normalize_tensor(state, state_norm_arr): + ''' Retrieve state from normalized state - tensor ''' + state_time = tf.concat([tf.zeros([state.shape[0], state.shape[1]-1]), tf.reshape((state[:,-1]+1)*state_norm_arr[-1]/2,[state.shape[0],1])],1) + state_no_time = state * state_norm_arr + mask = tf.concat([tf.ones([state.shape[0], state.shape[1]-1]), tf.zeros([state.shape[0], 1])],1) + state_not_norm = state_no_time * mask + state_time * (1 - mask) + + return state_not_norm + +def normalize_tensor(state, state_norm_arr): + ''' Retrieve state from normalized state - tensor ''' + state_norm_time = tf.concat([tf.zeros([state.shape[0], state.shape[1]-1]), tf.reshape(((state[:,-1]) / state_norm_arr[-1])*2 - 1,[state.shape[0],1])],1) + state_norm_no_time = state / state_norm_arr + mask = tf.concat([tf.ones([state.shape[0], state.shape[1]-1]), tf.zeros([state.shape[0], 1])],1) + state_norm = state_norm_no_time * mask + state_norm_time * (1 - mask) + + return state_norm + +def de_normalize(state, state_norm_arr): + ''' Retrieve state from normalized state ''' + state_not_norm = np.empty_like(state) + state_not_norm[:-1] = state[:-1] * state_norm_arr[:-1] + state_not_norm[-1] = (state[-1] + 1) * state_norm_arr[-1]/2 + + return state_not_norm + +def normalize(state, state_norm_arr): + ''' Normalize state ''' + state_norm = np.empty_like(state) + state_norm = state / state_norm_arr + state_norm[-1] = state_norm[-1] * 2 -1 + + return state_norm + + diff --git a/Results Single Integrator/Results set test/Code/N_try_1/NeuralNetwork.py b/Results Single Integrator/Results set test/Code/N_try_1/NeuralNetwork.py new file mode 100644 index 0000000..90f4127 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/NeuralNetwork.py @@ -0,0 +1,233 @@ +import math +import random +import numpy as np +import tensorflow as tf +from tensorflow.keras import layers, regularizers +from tf_siren import SinusodialRepresentationDense + +from utils import normalize_tensor + +class NN: + + def __init__(self, env, conf, w_S=0): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :param NH1: (int) 1st hidden layer size + :param NH2: (int) 2nd hidden layer size + :param kreg_l1_A : (float) Weight of L1 regularization in actor's network - kernel + :param kreg_l2_A : (float) Weight of L2 regularization in actor's network - kernel + :param breg_l1_A : (float) Weight of L2 regularization in actor's network - bias + :param breg_l2_A : (float) Weight of L2 regularization in actor's network - bias + :param kreg_l1_C : (float) Weight of L1 regularization in critic's network - kernel + :param kreg_l2_C : (float) Weight of L2 regularization in critic's network - kernel + :param breg_l1_C : (float) Weight of L1 regularization in critic's network - bias + :param breg_l2_C : (float) Weight of L2 regularization in critic's network - bias + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param NORMALIZE_INPUTS : (bool) Flag to normalize inputs (state) + :param state_norm_array : (float array) Array used to normalize states + :param MC : (bool) Flag to use MC or TD(n) + :param cost_weights_terminal : (float array) Running cost weights vector + :param cost_weights_running : (float array) Terminal cost weights vector + :param BATCH_SIZE : (int) Size of the mini-batch + :param dt : (float) Timestep + + :input w_S : (float) Sobolev-training weight + ''' + + self.env = env + self.conf = conf + + self.w_S = w_S + + self.MSE = tf.keras.losses.MeanSquaredError() + + return + + def create_actor(self): + ''' Create actor NN ''' + inputs = layers.Input(shape=(self.conf.nb_state,)) + + lay1 = layers.Dense(self.conf.NH1,kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(inputs) + leakyrelu1 = layers.LeakyReLU()(lay1) + lay2 = layers.Dense(self.conf.NH2, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(leakyrelu1) + leakyrelu2 = layers.LeakyReLU()(lay2) + outputs = layers.Dense(self.conf.nb_action, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(leakyrelu2) + + model = tf.keras.Model(inputs, outputs) + + return model + + def create_critic_elu(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = layers.Dense(16, activation='elu')(state_input) + state_out2 = layers.Dense(32, activation='elu')(state_out1) + out_lay1 = layers.Dense(256, activation='elu')(state_out2) + out_lay2 = layers.Dense(256, activation='elu')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_sine_elu(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = SinusodialRepresentationDense(64, activation='sine')(state_input) + state_out2 = layers.Dense(64, activation='elu')(state_out1) + out_lay1 = SinusodialRepresentationDense(128, activation='sine')(state_out2) + out_lay2 = layers.Dense(128, activation='elu')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_sine(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = SinusodialRepresentationDense(64, activation='sine')(state_input) + state_out2 = SinusodialRepresentationDense(64, activation='sine')(state_out1) + out_lay1 = SinusodialRepresentationDense(128, activation='sine')(state_out2) + out_lay2 = SinusodialRepresentationDense(128, activation='sine')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_relu(self): + ''' Create critic NN - relu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = layers.Dense(16, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(state_input) + leakyrelu1 = layers.LeakyReLU()(state_out1) + + state_out2 = layers.Dense(32, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu1) + leakyrelu2 = layers.LeakyReLU()(state_out2) + out_lay1 = layers.Dense(self.conf.NH1, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu2) + leakyrelu3 = layers.LeakyReLU()(out_lay1) + out_lay2 = layers.Dense(self.conf.NH2, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu3) + leakyrelu4 = layers.LeakyReLU()(out_lay2) + + outputs = layers.Dense(1, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu4) + + model = tf.keras.Model([state_input], outputs) + + return model + + def eval(self, NN, input): + ''' Compute the output of a NN given an input ''' + if not tf.is_tensor(input): + input = tf.convert_to_tensor(input, dtype=tf.float32) + + if self.conf.NORMALIZE_INPUTS: + input = normalize_tensor(input, self.conf.state_norm_arr) + + return NN(input, training=True) + + def custom_logarithm(self,input): + # Calculate the logarithms based on the non-zero condition + positive_log = tf.math.log(tf.math.maximum(input, 1e-7) + 1) + negative_log = -tf.math.log(tf.math.maximum(-input, 1e-7) + 1) + + # Use the appropriate logarithm based on the condition + result = tf.where(input > 0, positive_log, negative_log) + + return result + + def compute_critic_grad(self, critic_model, target_critic, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, weights_batch): + ''' Compute the gradient of the critic NN ''' + with tf.GradientTape() as tape: + # Compute value function tail if TD(n) is used + if self.conf.MC: + reward_to_go_batch = partial_reward_to_go_batch + else: + target_values = self.eval(target_critic, state_next_rollout_batch) # Compute Value at next state after conf.nsteps_TD_N steps given by target critic + reward_to_go_batch = partial_reward_to_go_batch + (1-d_batch)*target_values # Compute batch of 1-step targets for the critic loss + + # Compute critic loss + if self.w_S != 0: + with tf.GradientTape() as tape2: + tape2.watch(state_batch) + critic_value = self.eval(critic_model, state_batch) + der_critic_value = tape2.gradient(critic_value, state_batch) + + critic_loss_v = self.MSE(reward_to_go_batch, critic_value, sample_weight=weights_batch) + critic_loss_der = self.MSE(self.custom_logarithm(dVdx_batch[:,:-1]), self.custom_logarithm(der_critic_value[:,:-1]), sample_weight=weights_batch) # dV/dt not computed and so not used in the update + + critic_loss = critic_loss_der + self.w_S*critic_loss_v + else: + critic_value = self.eval(critic_model, state_batch) + critic_loss = self.MSE(reward_to_go_batch, critic_value, sample_weight=weights_batch) + + # Compute the gradients of the critic loss w.r.t. critic's parameters + critic_grad = tape.gradient(critic_loss, critic_model.trainable_variables) + + return critic_grad, reward_to_go_batch, critic_value, self.eval(target_critic, state_batch) + + def compute_actor_grad(self, actor_model, critic_model, state_batch, term_batch, batch_size): + ''' Compute the gradient of the actor NN ''' + if batch_size == None: + batch_size = self.conf.BATCH_SIZE + + actions = self.eval(actor_model, state_batch) + + # Both take into account normalization, ds_next_da is the gradient of the dynamics w.r.t. policy actions (ds'_da) + state_next_tf, ds_next_da = self.env.simulate_batch(state_batch.numpy(), actions.numpy()) , self.env.derivative_batch(state_batch.numpy(), actions.numpy()) + + with tf.GradientTape() as tape: + tape.watch(state_next_tf) + critic_value_next = self.eval(critic_model,state_next_tf) + + # dV_ds' = gradient of V w.r.t. s', where s'=f(s,a) a=policy(s) + dV_ds_next = tape.gradient(critic_value_next, state_next_tf) + + cost_weights_terminal_reshaped = np.reshape(self.conf.cost_weights_terminal,[1,len(self.conf.cost_weights_terminal)]) + cost_weights_running_reshaped = np.reshape(self.conf.cost_weights_running,[1,len(self.conf.cost_weights_running)]) + with tf.GradientTape() as tape1: + tape1.watch(actions) + rewards_tf = self.env.reward_batch(term_batch.dot(cost_weights_terminal_reshaped) + (1-term_batch).dot(cost_weights_running_reshaped), state_batch.numpy(), actions) + + # dr_da = gradient of reward r(s,a) w.r.t. policy's action a + dr_da = tape1.gradient(rewards_tf, actions, unconnected_gradients=tf.UnconnectedGradients.ZERO) + + dr_da_reshaped = tf.reshape(dr_da, (batch_size, 1, self.conf.nb_action)) + + # dr_ds' + dV_ds' (note: dr_ds' = 0) + dQ_ds_next = tf.reshape(dV_ds_next, (batch_size, 1, self.conf.nb_state)) + + # (dr_ds' + dV_ds')*ds'_da + dQ_ds_next_da = tf.matmul(dQ_ds_next, ds_next_da) + + # (dr_ds' + dV_ds')*ds'_da + dr_da + dQ_da = dQ_ds_next_da + dr_da_reshaped + + # Now let's multiply -[(dr_ds' + dV_ds')*ds'_da + dr_da] by the actions a + # and then let's autodifferentiate w.r.t theta_A (actor NN's parameters) to finally get -dQ/dtheta_A + with tf.GradientTape() as tape: + tape.watch(actor_model.trainable_variables) + actions = self.eval(actor_model, state_batch) + + actions_reshaped = tf.reshape(actions,(batch_size,self.conf.nb_action,1)) + dQ_da_reshaped = tf.reshape(dQ_da,(batch_size,1,self.conf.nb_action)) + Q_neg = tf.matmul(-dQ_da_reshaped,actions_reshaped) + + # Also here we need a scalar so we compute the mean -Q across the batch + mean_Qneg = tf.math.reduce_mean(Q_neg) + + # Gradients of the actor loss w.r.t. actor's parameters + actor_grad = tape.gradient(mean_Qneg, actor_model.trainable_variables) + + return actor_grad \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_1/RL.py b/Results Single Integrator/Results set test/Code/N_try_1/RL.py new file mode 100644 index 0000000..fa60798 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/RL.py @@ -0,0 +1,233 @@ +import uuid +import math +import numpy as np +import tensorflow as tf + +class RL_AC: + def __init__(self, env, NN, conf, N_try): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :parma critic_type : (str) Activation function to use for the critic NN + :param LR_SCHEDULE : (bool) Flag to use a scheduler for the learning rates + :param boundaries_schedule_LR_C : (list) Boudaries of critic LR + :param values_schedule_LR_C : (list) Values of critic LR + :param boundaries_schedule_LR_A : (list) Boudaries of actor LR + :param values_schedule_LR_A : (list) Values of actor LR + :param CRITIC_LEARNING_RATE : (float) Learning rate for the critic network + :param ACTOR_LEARNING_RATE : (float) Learning rate for the policy network + :param fresh_factor : (float) Refresh factor + :param prioritized_replay_alpha : (float) α determines how much prioritization is used + :param prioritized_replay_eps : (float) It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero + :param UPDATE_LOOPS : (int array) Number of updates of both critic and actor performed every EP_UPDATE episodes + :param save_interval : (int) save NNs interval + :param env_RL : (bool) Flag RL environment + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param MC : (bool) Flag to use MC or TD(n) + :param nsteps_TD_N : (int) Number of lookahed steps if TD(n) is used + :param UPDATE_RATE : (float) Homotopy rate to update the target critic network if TD(n) is used + :param cost_weights_terminal : (float array) Running cost weights vector + :param cost_weights_running : (float array) Terminal cost weights vector + :param dt : (float) Timestep + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param NNs_path : (str) NNs path + :param NSTEPS : (int) Max episode length + + ''' + self.env = env + self.NN = NN + self.conf = conf + + self.N_try = N_try + + self.actor_model = None + self.critic_model = None + self.target_critic = None + self.actor_optimizer = None + self.critic_optimizer = None + + self.init_rand_state = None + self.NSTEPS_SH = 0 + self.control_arr = None + self.state_arr = None + self.ee_pos_arr = None + self.exp_counter = np.zeros(conf.REPLAY_SIZE) + + return + + def setup_model(self, recover_training=None): + ''' Setup RL model ''' + # Create actor, critic and target NNs + self.actor_model = self.NN.create_actor() + + if self.conf.critic_type == 'elu': + self.critic_model = self.NN.create_critic_elu() + self.target_critic = self.NN.create_critic_elu() + elif self.conf.critic_type == 'sine': + self.critic_model = self.NN.create_critic_sine() + self.target_critic = self.NN.create_critic_sine() + elif self.conf.critic_type == 'sine-elu': + self.critic_model = self.NN.create_critic_sine_elu() + self.target_critic = self.NN.create_critic_sine_elu() + else: + self.critic_model = self.NN.create_critic_relu() + self.target_critic = self.NN.create_critic_relu() + + # Set optimizer specifying the learning rates + if self.conf.LR_SCHEDULE: + # Piecewise constant decay schedule + self.CRITIC_LR_SCHEDULE = tf.keras.optimizers.schedules.PiecewiseConstantDecay(self.conf.boundaries_schedule_LR_C, self.conf.values_schedule_LR_C) + self.ACTOR_LR_SCHEDULE = tf.keras.optimizers.schedules.PiecewiseConstantDecay(self.conf.boundaries_schedule_LR_A, self.conf.values_schedule_LR_A) + self.critic_optimizer = tf.keras.optimizers.Adam(self.CRITIC_LR_SCHEDULE) + self.actor_optimizer = tf.keras.optimizers.Adam(self.ACTOR_LR_SCHEDULE) + else: + self.critic_optimizer = tf.keras.optimizers.Adam(self.conf.CRITIC_LEARNING_RATE) + self.actor_optimizer = tf.keras.optimizers.Adam(self.conf.ACTOR_LEARNING_RATE) + + # Set initial weights of the NNs + if recover_training is not None: + NNs_path_rec = str(recover_training[0]) + N_try = recover_training[1] + update_step_counter = recover_training[2] + self.actor_model.load_weights("{}/N_try_{}/actor_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + self.critic_model.load_weights("{}/N_try_{}/critic_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + self.target_critic.load_weights("{}/N_try_{}/target_critic_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + else: + self.target_critic.set_weights(self.critic_model.get_weights()) + + def update(self, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, term_batch, weights_batch, batch_size=None): + ''' Update both critic and actor ''' + # Update the critic backpropagating the gradients + critic_grad, reward_to_go_batch, critic_value, target_critic_value = self.NN.compute_critic_grad(self.critic_model, self.target_critic, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, weights_batch) + self.critic_optimizer.apply_gradients(zip(critic_grad, self.critic_model.trainable_variables)) + + # Update the actor backpropagating the gradients + actor_grad = self.NN.compute_actor_grad(self.actor_model, self.critic_model, state_batch, term_batch, batch_size) + self.actor_optimizer.apply_gradients(zip(actor_grad, self.actor_model.trainable_variables)) + + return reward_to_go_batch, critic_value, target_critic_value + + @tf.function + def update_target(self, target_weights, weights): + ''' Update target critic NN ''' + tau = self.conf.UPDATE_RATE + for (a, b) in zip(target_weights, weights): + a.assign(b * tau + a * (1 - tau)) + + def learn_and_update(self, update_step_counter, buffer, ep): + ''' Sample experience and update buffer priorities and NNs ''' + for i in range(int(self.conf.UPDATE_LOOPS[ep])): + # Sample batch of transitions from the buffer + state_batch, partial_reward_to_go_batch, state_next_rollout_batch, dVdx_batch, d_batch, term_batch, weights_batch, batch_idxes = buffer.sample() + + # Update both critic and actor + reward_to_go_batch, critic_value, target_critic_value = self.update(state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, term_batch, weights_batch) + + # Update buffer priorities + if self.conf.prioritized_replay_alpha != 0: + buffer.update_priorities(batch_idxes, reward_to_go_batch, critic_value, target_critic_value) + + # Update target critic + if not self.conf.MC: + self.update_target(self.target_critic.variables, self.critic_model.variables) + + update_step_counter += 1 + + # Plot rollouts and save the NNs every conf.log_rollout_interval-training episodes + if update_step_counter%self.conf.save_interval == 0: + self.RL_save_weights(update_step_counter) + + return update_step_counter + + def RL_Solve(self, TO_controls, TO_states, TO_step_cost): + ''' Solve RL problem ''' + ep_return = 0 # Initialize the return + rwrd_arr = np.empty(self.NSTEPS_SH+1) # Reward array + state_next_rollout_arr = np.zeros((self.NSTEPS_SH+1, self.conf.nb_state)) # Next state array + partial_reward_to_go_arr = np.empty(self.NSTEPS_SH+1) # Partial cost-to-go array + total_reward_to_go_arr = np.empty(self.NSTEPS_SH+1) # Total cost-to-go array + term_arr = np.zeros(self.NSTEPS_SH+1) # Episode-termination flag array + term_arr[-1] = 1 + done_arr = np.zeros(self.NSTEPS_SH+1) # Episode-MC-termination flag array + + # START RL EPISODE + self.control_arr = TO_controls # action clipped in TO + + if self.conf.env_RL: + for step_counter in range(self.NSTEPS_SH): + # Simulate actions and retrieve next state and compute reward + self.state_arr[step_counter+1,:], rwrd_arr[step_counter] = self.env.step(self.conf.cost_weights_running, self.state_arr[step_counter,:], self.control_arr[step_counter,:]) + + # Compute end-effector position + self.ee_pos_arr[step_counter+1,:] = self.env.get_end_effector_position(self.state_arr[step_counter+1, :]) + rwrd_arr[-1] = self.env.reward(self.conf.cost_weights_terminal, self.state_arr[-1,:]) + else: + self.state_arr, rwrd_arr = TO_states, -TO_step_cost + + ep_return = sum(rwrd_arr) + + # Store transition after computing the (partial) cost-to go when using n-step TD (from 0 to Monte Carlo) + for i in range(self.NSTEPS_SH+1): + # set final lookahead step depending on whether Monte Cartlo or TD(n) is used + if self.conf.MC: + final_lookahead_step = self.NSTEPS_SH + done_arr[i] = 1 + else: + final_lookahead_step = min(i+self.conf.nsteps_TD_N, self.NSTEPS_SH) + if final_lookahead_step == self.NSTEPS_SH: + done_arr[i] = 1 + else: + state_next_rollout_arr[i,:] = self.state_arr[final_lookahead_step+1,:] + + # Compute the partial and total cost to go + partial_reward_to_go_arr[i] = np.float32(sum(rwrd_arr[i:final_lookahead_step+1])) + total_reward_to_go_arr[i] = np.float32(sum(rwrd_arr[i:self.NSTEPS_SH+1])) + + return self.state_arr, partial_reward_to_go_arr, total_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, self.ee_pos_arr + + def RL_save_weights(self, update_step_counter='final'): + ''' Save NN weights ''' + self.actor_model.save_weights(self.conf.NNs_path+"/N_try_{}/actor_{}.h5".format(self.N_try,update_step_counter)) + self.critic_model.save_weights(self.conf.NNs_path+"/N_try_{}/critic_{}.h5".format(self.N_try,update_step_counter)) + self.target_critic.save_weights(self.conf.NNs_path+"/N_try_{}/target_critic_{}.h5".format(self.N_try,update_step_counter)) + + def create_TO_init(self, ep, ICS): + ''' Create initial state and initial controls for TO ''' + self.init_rand_state = ICS + + self.NSTEPS_SH = self.conf.NSTEPS - int(self.init_rand_state[-1]/self.conf.dt) + if self.NSTEPS_SH == 0: + return None, None, None, None, 0 + + # Initialize array to store RL state, control, and end-effector trajectories + self.control_arr = np.empty((self.NSTEPS_SH, self.conf.nb_action)) + self.state_arr = np.empty((self.NSTEPS_SH+1, self.conf.nb_state)) + self.ee_pos_arr = np.empty((self.NSTEPS_SH+1,3)) + + # Set initial state and end-effector position + self.state_arr[0,:] = self.init_rand_state + self.ee_pos_arr[0,:] = self.env.get_end_effector_position(self.state_arr[0, :]) + + # Initialize array to initialize TO state and control variables + init_TO_controls = np.zeros((self.NSTEPS_SH, self.conf.nb_action)) + init_TO_states = np.zeros(( self.NSTEPS_SH+1, self.conf.nb_state)) + + # Set initial state + init_TO_states[0,:] = self.init_rand_state + + # Simulate actor's actions to compute the state trajectory used to initialize TO state variables (use ICS for state and 0 for control if it is the first episode otherwise use policy rollout) + success_init_flag = 1 + for i in range(self.NSTEPS_SH): + if ep == 0: + init_TO_controls[i,:] = np.zeros(self.conf.nb_action) + else: + init_TO_controls[i,:] = tf.squeeze(self.NN.eval(self.actor_model, np.array([init_TO_states[i,:]]))).numpy() + init_TO_states[i+1,:] = self.env.simulate(init_TO_states[i,:],init_TO_controls[i,:]) + if np.isnan(init_TO_states[i+1,:]).any(): + success_init_flag = 0 + return None, None, None, None, success_init_flag + + return self.init_rand_state, init_TO_states, init_TO_controls, self.NSTEPS_SH, success_init_flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_1/TO.py b/Results Single Integrator/Results set test/Code/N_try_1/TO.py new file mode 100644 index 0000000..1933f7a --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/TO.py @@ -0,0 +1,201 @@ +import sys +import math +import casadi +import numpy as np +import pinocchio.casadi as cpin + +class TO_Casadi: + + def __init__(self, env, conf, env_TO, w_S=0): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param u_min : (float array) Action lower bound array + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param dt : (float) Timestep + + :input system_id : (str) Id system + + :input w_S : (float) Sobolev-training weight + ''' + + self.env = env + self.conf = conf + + self.nx = conf.nx + self.nu = conf.na + + self.w_S = w_S + + self.CAMS = env_TO + + def TO_System_Solve(self, ICS_state, init_TO_states, init_TO_controls, T): + ''' Create and solbe TO casadi problem ''' + ### PROBLEM + opti = casadi.Opti() + + # The control models are stored as a collection of shooting nodes called running models, with an additional terminal model. + self.runningSingleModel = self.CAMS('running_model', self.conf) + runningModels = [ self.runningSingleModel for _ in range(T) ] + self.terminalModel = self.CAMS('terminal_model', self.conf) + + # Decision variables + xs = [ opti.variable(model.nx) for model in runningModels+[self.terminalModel] ] # state variable + us = [ opti.variable(model.nu) for model in runningModels ] # control variable + + # Roll out loop, summing the integral cost and defining the shooting constraints. + total_cost = 0 + + opti.subject_to(xs[0] == ICS_state[:-1]) + + for t in range(T): + x_next, r_cost = runningModels[t].step_fun(xs[t], us[t]) + opti.subject_to(xs[t + 1] == x_next ) + total_cost += r_cost + r_cost_final = self.terminalModel.cost(xs[-1], us[-1]) + total_cost += r_cost_final + + ### SOLVE + opti.minimize(total_cost) + + # Create warmstart + init_x_TO = [np.array(init_TO_states[i,:-1]) for i in range(T+1)] + init_u_TO = [np.array(init_TO_controls[i,:]) for i in range(T)] + + for x,xg in zip(xs,init_x_TO): opti.set_initial(x,xg) + for u,ug in zip(us,init_u_TO): opti.set_initial(u,ug) + + # Set solver options + opts = {'ipopt.linear_solver':'ma57', 'ipopt.sb': 'yes','ipopt.print_level': 0, 'print_time': 0} #, 'ipopt.max_iter': 500} + opti.solver("ipopt", opts) + + try: + opti.solve() + TO_states = np.array([ opti.value(x) for x in xs ]) + TO_controls = np.array([ opti.value(u) for u in us ]) + TO_total_cost = opti.value(total_cost) + TO_ee_pos_arr = np.empty((T+1,3)) + TO_step_cost = np.empty(T+1) + for n in range(T): + TO_ee_pos_arr[n,:] = np.reshape(runningModels[n].p_ee(TO_states[n,:]),-1) + TO_step_cost[n] = runningModels[n].cost(TO_states[n,:], TO_controls[n,:]) + TO_ee_pos_arr[-1,:] = np.reshape(self.terminalModel.p_ee(TO_states[-1,:]),-1) + TO_step_cost[-1] = self.terminalModel.cost(TO_states[-1,:], TO_controls[-1,:]) + success_flag = 1 + except: + print('ERROR in convergence, returning debug values') + TO_states = np.array([ opti.debug.value(x) for x in xs ]) + TO_controls = np.array([ opti.debug.value(u) for u in us ]) + TO_total_cost = None + TO_ee_pos_arr = None + TO_step_cost = None + success_flag = 0 + + return success_flag, TO_controls, TO_states, TO_ee_pos_arr, TO_total_cost, TO_step_cost + + def TO_Solve(self, ICS_state, init_TO_states, init_TO_controls, T): + ''' Retrieve TO problem solution and compute the value function derviative with respect to the state ''' + success_flag, TO_controls, TO_states, TO_ee_pos_arr, _, TO_step_cost = self.TO_System_Solve(ICS_state, init_TO_states, init_TO_controls, T) + if success_flag == 0: + return None, None, success_flag, None, None, None + + if self.w_S != 0: + # Compute V gradient w.r.t. x (no computation dV/dt) + dVdx = self.backward_pass(T+1, TO_states, TO_controls) + else: + dVdx = np.zeros((T+1, self.conf.nb_state)) + + # Add the last state component (time) + TO_states = np.concatenate((TO_states, init_TO_states[0,-1] + np.transpose(self.conf.dt*np.array([range(T+1)]))), axis=1) + + return TO_controls, TO_states, success_flag, TO_ee_pos_arr, TO_step_cost, dVdx + + def backward_pass(self, T, TO_states, TO_controls, mu=1e-9): + ''' Perform the backward-pass of DDP to obtain the derivatives of the Value function w.r.t x ''' + n = self.conf.nb_state-1 + m = self.conf.nb_action + + X_bar = np.zeros((T, n)) + for i in range(n): + X_bar[:,i] = [TO_states[t,i] for t in range(T)] + + U_bar = np.zeros((T-1, m)) + for i in range(m): + U_bar[:,i] = [TO_controls[t,i] for t in range(T-1)] + + # The task is defined by a quadratic cost: + # sum_{i=0}^T 0.5 x' l_{xx,i} x + l_{x,i} x + 0.5 u' l_{uu,i} u + l_{u,i} u + x' l_{xu,i} u + l_x = np.zeros((T, n)) + l_xx = np.zeros((T, n, n)) + l_u = np.zeros((T-1, m)) + l_uu = np.zeros((T-1, m, m)) + l_xu = np.zeros((T-1, n, m)) + + # The cost-to-go is defined by a quadratic function: 0.5 x' Q_{xx,i} x + Q_{x,i} x + ... + Q_xx = np.zeros((T-1, n, n)) + Q_x = np.zeros((T-1, n)) + Q_uu = np.zeros((T-1, m, m)) + Q_u = np.zeros((T-1, m)) + Q_xu = np.zeros((T-1, n, m)) + + x = casadi.SX.sym('x',n,1) + u = casadi.SX.sym('u',m,1) + + running_cost = -self.runningSingleModel.cost(x, u) + terminal_cost = -self.terminalModel.cost(x, u) + + running_cost_xx, running_cost_x = casadi.hessian(running_cost,x) + running_cost_uu, running_cost_u = casadi.hessian(running_cost,u) + running_cost_xu = casadi.jacobian(casadi.jacobian(running_cost,x),u) + terminal_cost_xx, terminal_cost_x = casadi.hessian(terminal_cost,x) + + fun_running_cost_x = casadi.Function('fun_running_cost_x', [x], [running_cost_x], ['x'], ['running_cost_x']) + fun_running_cost_xx = casadi.Function('fun_running_cost_xx', [x], [running_cost_xx], ['x'], ['running_cost_xx']) + fun_running_cost_xu = casadi.Function('fun_running_cost_xu', [x,u],[running_cost_xu], ['x','u'], ['running_cost_xu']) + fun_running_cost_u = casadi.Function('fun_running_cost_u', [u], [running_cost_u], ['u'], ['running_cost_u']) + fun_running_cost_uu = casadi.Function('fun_running_cost_uu', [u], [running_cost_uu], ['u'], ['running_cost_uu']) + fun_terminal_cost_x = casadi.Function('fun_terminal_cost_x', [x], [terminal_cost_x], ['x'], ['terminal_cost_x']) + fun_terminal_cost_xx = casadi.Function('fun_terminal_cost_xx',[x], [terminal_cost_xx], ['x'], ['terminal_cost_xx']) + + # The Value function is defined by a quadratic function: 0.5 x' V_{xx,i} x + V_{x,i} x + V_xx = np.zeros((T, n, n)) + V_x = np.zeros((T, n+1)) + + # Dynamics derivatives w.r.t. x and u + A = np.zeros((T-1, n, n)) + B = np.zeros((T-1, n, m)) + + # Initialize value function + l_x[-1,:], l_xx[-1,:,:] = np.reshape(fun_terminal_cost_x(X_bar[-1,:]),n), fun_terminal_cost_xx(X_bar[-1,:]) + V_xx[T-1,:,:] = l_xx[-1,:,:] + V_x[T-1,:-1] = l_x[-1,:] + + for i in range(T-2, -1, -1): + # Compute dynamics Jacobians + A[i,:,:], B[i,:,:] = self.env.augmented_derivative(X_bar[i,:], U_bar[i,:]) + + # Compute the gradient of the cost function at X=X_bar + l_x[i,:], l_xx[i,:,:] = np.reshape(fun_running_cost_x(X_bar[i,:]),n), fun_running_cost_xx(X_bar[i,:]) + l_u[i,:],l_uu[i,:,:] = np.reshape(fun_running_cost_u(U_bar[i,:]),m), fun_running_cost_uu(U_bar[i,:]) + l_xu[i,:,:] = fun_running_cost_xu(X_bar[i,:], U_bar[i,:]) + + # Compute regularized cost-to-go + Q_x[i,:] = l_x[i,:] + A[i,:,:].T @ V_x[i+1,:-1] + Q_u[i,:] = l_u[i,:] + B[i,:,:].T @ V_x[i+1,:-1] + Q_xx[i,:,:] = l_xx[i,:,:] + A[i,:,:].T @ V_xx[i+1,:,:] @ A[i,:,:] + Q_uu[i,:,:] = l_uu[i,:,:] + B[i,:,:].T @ V_xx[i+1,:,:] @ B[i,:,:] + Q_xu[i,:,:] = l_xu[i,:,:] + A[i,:,:].T @ V_xx[i+1,:,:] @ B[i,:,:] + + Qbar_uu = Q_uu[i,:,:] + mu*np.identity(m) + Qbar_uu_pinv = np.linalg.pinv(Qbar_uu) + + # Compute the derivative of the Value function w.r.t. x + V_x[i,:-1] = Q_x[i,:] - Q_xu[i,:,:] @ Qbar_uu_pinv @ Q_u[i,:] + V_xx[i,:] = Q_xx[i,:] - Q_xu[i,:,:] @ Qbar_uu_pinv @ Q_xu[i,:,:].T + + return V_x \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_1/conf_car.py b/Results Single Integrator/Results set test/Code/N_try_1/conf_car.py new file mode 100644 index 0000000..c4d72bb --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/conf_car.py @@ -0,0 +1,203 @@ +import math +import numpy as np + +system_id = 'car' + +''' CACTO parameters ''' +EP_UPDATE = 250 # Number of episodes before updating critic and actor +NUPDATES = 260000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 38000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 500 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 10000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 1e2 # Distance from target weight +w_u = 1e1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Car/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Car/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Car/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Car/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Car/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Car/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Car/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' # Link mass + +### State parameters +nb_state = 5 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 5 +na = 2 +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, -math.pi, -10, -3, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, math.pi, 10, 3, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, math.pi, 10, 3, int(NSTEPS*dt)]) # Array used to normalize states +# state: x, y, theta, v, a, t + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-10.0, 10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-10.0, -10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([12.0, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([12.0, -2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([15.0, 0.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +bound_actions = 0 +omega_lower_bound = -2 # Action lower bound +omega_upper_bound = 2 +jerk_lower_bound = -1 +jerk_upper_bound = 1 # Action upper bound +u_min = np.array([omega_lower_bound, jerk_lower_bound]) # Action lower bound vector +u_max = np.array([omega_upper_bound, jerk_upper_bound]) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_1/conf_car_park.py b/Results Single Integrator/Results set test/Code/N_try_1/conf_car_park.py new file mode 100644 index 0000000..05c66c1 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/conf_car_park.py @@ -0,0 +1,221 @@ +import math +import numpy as np + +system_id = 'car_park' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 260000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 38000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/2) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 10000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 0 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -10 # X coord center ellipse 1 +YC1 = 6.75 # Y coord center ellipse 1 +A1 = 17 # Width ellipse 1 +B1 = 4.5 # Height ellipse 1 +XC2 = 10 # X coord center ellipse 2 +YC2 = 6.75 # Y coord center ellipse 2 +A2 = 17 # Width ellipse 2 +B2 = 4.5 # Height ellipse 2 +XC3 = 0 # X coord center ellipse 2 +YC3 = -2 # Y coord center ellipse 2 +A3 = 40 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +L = 4.35 +W = 2 +L_delta = 2.63 +tau_delta = 1 + +check_points_BF = np.array([[-L/2, W/2], + [-L/2+L/3, W/2], + [-L/2+2/3*L, W/2], + [L/2, W/2], + [L/2, 0], + [L/2, -W/2], + [-L/2+2/3*L, -W/2], + [-L/2+L/3, -W/2], + [-L/2, -W/2], + [-L/2, 0]]) # Check points in BF # Check points in BF + +### Weigths +w_d = 1e2 # Distance from target weight +w_u = 1e1 # Control effort weight +w_peak = 1e6 # Target threshold weight +w_ob = 5e4 # Obstacle weight +w_v = 1e2 # Velocity weight +delta_bound = 2*np.pi/6 +w_delta_bound = 0 # Delta bound weight (not implemented) +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u, w_delta_bound]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, w_v, w_ob, w_ob, w_ob, 0, w_delta_bound]) # Terminal cost weights vector + +k_db = 50 + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 1 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = 0 # Target x position +y_des = 6.75 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Car Park/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Car Park/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Car Park/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Car Park/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Car Park/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Car Park/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Car Park/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' # Link mass + +### State parameters +nb_state = 5 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 5 +na = 2 + +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-10, 1.5, -math.pi/6, 0, 0, 0]) #1.5 #-2.5 # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 10, 3, math.pi/6, 0, 0, (NSTEPS-1)*dt]) #3.0 #10 # State upper bound initial configuration array +state_norm_arr = np.array([10, 3, math.pi, 10, math.pi/6, int(NSTEPS*dt)]) # Array used to normalize states +# state: x, y, theta, v, delta, t + +# initial configurations for plot.rollout() +init_states_sim = [np.array([-9.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-5.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-2.5 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([0.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([2.5 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([5.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([9.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +acc_lower_bound = -3 # Action lower bound +acc_upper_bound = 3 +delta_dot_lower_bound = -1 +delta_dot_upper_bound = 1 # Action upper bound +u_min = np.array([acc_lower_bound, delta_dot_lower_bound]) # Action lower bound vector +u_max = np.array([acc_upper_bound, delta_dot_upper_bound]) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-11, 11], [-2.5, 10]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_1/conf_double_integrator.py b/Results Single Integrator/Results set test/Code/N_try_1/conf_double_integrator.py new file mode 100644 index 0000000..03b56ca --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/conf_double_integrator.py @@ -0,0 +1,213 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'double_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 50000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 18000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 200 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-4 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 1 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Double Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Double Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Double Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Double Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Double Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Double Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Double Integrator/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Robot upload data +URDF_FILENAME = "double_integrator.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + +q_init, v_init = np.array([-5, 0]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max) + +### State parameters +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, dt]) # State lower bound vector +x_init_min = np.array([-15, -15, -6, -6, dt]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, 6, 6, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, 6, 6, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([12.0, 2.0, 0.0, 0.0, 0.0]), + np.array([12.0, -2.0, 0.0, 0.0, 0.0]), + np.array([15.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = robot.na # Action size +tau_lower_bound = -2 # Action lower bound +tau_upper_bound = 2 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-15, 15], [-15, 15]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_1/conf_manipulator.py b/Results Single Integrator/Results set test/Code/N_try_1/conf_manipulator.py new file mode 100644 index 0000000..6b5d539 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/conf_manipulator.py @@ -0,0 +1,225 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'manipulator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 380000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 50000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network + nsteps_TD_N = int(NSTEPS/2) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 15000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 0 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 24000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' + +NH1 = 256 # 1st hidden layer size +NH2 = 256 # 2nd hidden layer size + +LR_SCHEDULE = 1 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 1e4 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, w_v, w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 50 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -20.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Manipulator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Manipulator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Manipulator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Manipulator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Manipulator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Manipulator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Manipulator/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Robot upload data +URDF_FILENAME = "planar_manipulator_3dof.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + +q_init, v_init = np.array([math.pi, math.pi, math.pi]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max) + +### System configuration parameters +x_base = -7.0 # x coord base +y_base = 0.0 # y coord base + +### State parameters +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-math.pi, -math.pi, -math.pi, -math.pi/4, -math.pi/4, -math.pi/4, 0]) # State lower bound initial configuration vector +x_max = np.array([ np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ math.pi, math.pi, math.pi, math.pi/4, math.pi/4, math.pi/4, (NSTEPS-1)*dt]) # State upper bound initial configuration vector +state_norm_arr = np.array([15,15,15,10,10,10,int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([math.pi/4, -math.pi/8, -math.pi/8, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, math.pi/8, math.pi/8, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + #np.array([ 1.34565955, -2.39833441, 0.87800266, 0., 0., 0., 0. ]), + #np.array([-1.34565955, 2.39833441, -0.87800266, 0., 0., 0., 0. ]), + np.array([-1.55135003, 2.93707696, -1.3025857 , 0., 0., 0., 0. ]), + np.array([ 1.55135003, -2.93707696, 1.3025857 , 0., 0., 0., 0. ]), + np.array([-1.31811607, 2.63623214, -1.31811607, 0., 0., 0., 0. ]), + np.array([-0.98843209, 1.97686418, -0.98843209, 0., 0., 0., 0. ])] + #np.array([-1.05348883, 1.9057266 , -0.61849459, 0., 0., 0., 0. ])] + #np.array([ 1.05348883, -1.9057266 , 0.61849459, 0., 0., 0., 0. ])] + +### Action parameters +nb_action = robot.na # Action size +tau_lower_bound = -200 # Action lower bound +tau_upper_bound = 200 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-41, 31], [-35, 35]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_1/conf_single_integrator.py b/Results Single Integrator/Results set test/Code/N_try_1/conf_single_integrator.py new file mode 100644 index 0000000..02f767d --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/conf_single_integrator.py @@ -0,0 +1,200 @@ +import numpy as np + +system_id = 'single_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 100000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 25000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Single Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Single Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Single Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Single Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Single Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Single Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = 'set prova b' +NNs_path_rec = './Results Single Integrator/Results {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = 0 +update_step_counter_rec = 12000 + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + + + +### State parameters +nb_state = 2 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 2 +na = 2 +x_min = np.array([-np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0]), + np.array([10.0, 10.0, 0.0]), + np.array([-10.0, 10.0, 0.0]), + np.array([-10.0, -10.0, 0.0]), + np.array([12.0, 2.0, 0.0]), + np.array([12.0, -2.0, 0.0]), + np.array([15.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +tau_lower_bound = -6 # Action lower bound +tau_upper_bound = 6 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_1/conf_ur5.py b/Results Single Integrator/Results set test/Code/N_try_1/conf_ur5.py new file mode 100644 index 0000000..1521ac0 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/conf_ur5.py @@ -0,0 +1,227 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'ur5' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 380000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 50000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + # 2nd hidden layer size + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +# Obstacles parameters +XC1 = 0.0 # X coord center ellipse 1 +YC1 = 0.25 # Y coord center ellipse 1 +ZC1 = 0.2 +ell1_center = [XC1, YC1, ZC1] +A1 = 0.5 # Width ellipse 1 +B1 = 0.2 # Height ellipse 1 +C1 = 0.34 +XC2 = 0.2 # X coord center ellipse 2 +YC2 = 0.425 # Y coord center ellipse 2 +ZC2 = 0.2 +ell2_center = [XC2, YC2, ZC2] +A2 = 0.4 # Width ellipse 2 +B2 = 0.14 # Height ellipse 2 +C2 = 0.34 +XC3 = -0.2 # X coord center ellipse 2 +YC3 = 0.425 # Y coord center ellipse 2 +ZC3 = 0.2 +ell3_center = [XC3, YC3, ZC3] +A3 = 0.4 # Width ellipse 2 +B3 = 0.14 # Height ellipse 2 +C3 = 0.34 +obs_param = np.array([XC1, YC1, ZC1, XC2, YC2, ZC2, XC3, YC3, ZC3, A1, B1, C1, A2, B2, C2, A3, B3, C3]) # Obstacle parameters vector + +# Weigths +w_d = 100 # Distance from target weight +w_u = 1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +# SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +# Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +# Target parameters +x_des = 0.0 # Target x position +y_des = 0.425 # Target y position +z_des = 0.2 +TARGET_STATE = np.array([x_des, y_des, z_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results UR5/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results UR5/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results UR5/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results UR5/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results UR5/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results UR5/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results UR5/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + + + +''' System parameters ''' +# Robot upload data +URDF_FILENAME = "ur5_robot.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +# Dynamics parameters' +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' +use_viewer = False +simulate_real_time = True +show_floor = False +PRINT_T = 1 # print every PRINT_N time steps +DISPLAY_T = 0.02 # update robot configuration in viewer every DISPLAY_N time steps +# CAMERA_TRANSFORM = [3.6914889812469482, 0.4583563506603241, -0.05435386672616005, 0.48037904500961304, 0.5339481830596924, 0.5137122273445129, 0.4692920446395874] +CAMERA_TRANSFORM = [0.36461642384529114, 0.8866147994995117, 2.579286575317383, 0.03584412857890129, 0.14833784103393555, 0.9481053948402405, 0.27893954515457153] + +q_init, v_init = np.array([0.,-math.pi/2,0.,0.,0.,0.]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max, use_viewer, DISPLAY_T, CAMERA_TRANSFORM, show_floor) + +# State parameters +dt = 0.01 # Timestep +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-math.pi, -math.pi, -math.pi, -math.pi, -math.pi, -math.pi, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, 0]) # State lower bound initial configuration vector +x_max = np.array([ np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ math.pi, math.pi, math.pi, math.pi, math.pi, math.pi, math.pi/4, math.pi/4, math.pi/4, math.pi/4, math.pi/4, math.pi/4, (NSTEPS-1)*dt]) # State upper bound initial configuration vector +state_norm_arr = np.array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([math.pi/4, -math.pi/8, -math.pi/8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, math.pi/8, math.pi/8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])] + +# Action parameters +nb_action = robot.na # Action size +u_min = np.array([-150, -150, -150, -28, -28, -28]) # Action lower bound vector +u_max = np.array([150, 150, 150, 28, 28, 28]) # Action upper bound vector +w_b = 1/w_u + + +fig_ax_lim = np.array([[-3, 3], [-3, 3]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + +profile = 0 + +env_RL = 0 diff --git a/Results Single Integrator/Results set test/Code/N_try_1/environment.py b/Results Single Integrator/Results set test/Code/N_try_1/environment.py new file mode 100644 index 0000000..87bd97f --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/environment.py @@ -0,0 +1,816 @@ +import math +import mpmath +import random +import numpy as np +import tensorflow as tf +import pinocchio as pin + +from utils import * + +class Env: + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param simu : (RobotSimulator instance) + :param x_init_min : (float array) State lower bound initial configuration array + :param x_init_max : (float array) State upper bound initial configuration array + :param x_min : (float array) State lower bound vector + :param x_max : (float array) State upper bound vector + :param u_min : (float array) Action lower bound array + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + ''' + + self.conf = conf + + self.nq = conf.nq + self.nv = conf.nv + self.nx = conf.nx + self.nu = conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + def reset(self): + ''' Choose initial state uniformly at random ''' + state = np.zeros(self.conf.nb_state) + + time = random.uniform(self.conf.x_init_min[-1], self.conf.x_init_max[-1]) + for i in range(self.conf.nb_state-1): + state[i] = random.uniform(self.conf.x_init_min[i], self.conf.x_init_max[i]) + state[-1] = self.conf.dt*round(time/self.conf.dt) + + return state + + def check_ICS_feasible(self, state): + ''' Check if ICS is not feasible ''' + # check if ee is in the obstacles + p_ee = self.get_end_effector_position(state) + + ellipse1 = ((p_ee[0] - self.conf.XC1)**2) / ((self.conf.A1 / 2)**2) + ((p_ee[1] - self.conf.YC1)**2) / ((self.conf.B1 / 2)**2) + ellipse2 = ((p_ee[0] - self.conf.XC2)**2) / ((self.conf.A2 / 2)**2) + ((p_ee[1] - self.conf.YC2)**2) / ((self.conf.B2 / 2)**2) + ellipse3 = ((p_ee[0] - self.conf.XC3)**2) / ((self.conf.A3 / 2)**2) + ((p_ee[1] - self.conf.YC3)**2) / ((self.conf.B3 / 2)**2) + + feasible_flag = ellipse1 > 1 and ellipse2 > 1 and ellipse3 > 1 + + return feasible_flag + + def step(self, weights, state, action): + ''' Return next state and reward ''' + # compute next state + state_next = self.simulate(state, action) + + # compute reward + reward = self.reward(weights, state, action) + + return (state_next, reward) + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + # Simulate control action + self.conf.simu.simulate(np.copy(state[:-1]), action, self.conf.dt, 1) ### Explicit Euler ### + + # Return next state + state_next[:self.nq], state_next[self.nq:self.nx] = np.copy(self.conf.simu.q), np.copy(self.conf.simu.v) + state_next[-1] = state[-1] + self.conf.dt + + return state_next + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Create robot model in Pinocchio with q_init as initial configuration + q_init = state[:self.nq] + v_init = state[self.nq:self.nx] + + # Dynamics gradient w.r.t control (1st order euler) + pin.computeABADerivatives(self.conf.robot.model, self.conf.robot.data, np.copy(q_init), np.copy(v_init), action) + + Fu = np.zeros((self.nx+1, self.nu)) + Fu[self.nv:-1, :] = self.conf.robot.data.Minv + Fu[:self.nx, :] *= self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + q = state[:self.nq] + v = state[self.nq:self.nx] + + # Compute Jacobians for continuous time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + pin.computeABADerivatives(self.conf.robot.model, self.conf.robot.data, q, v, action) + + Fx[:self.nv, :self.nv] = 0.0 + Fx[:self.nv, self.nv:self.nx] = np.identity(self.nv) + Fx[self.nv:self.nx, :self.nv] = self.conf.robot.data.ddq_dq + Fx[self.nv:self.nx, self.nv:self.nx] = self.conf.robot.data.ddq_dv + Fu[self.nv:self.nx, :] = self.conf.robot.data.Minv + + # Convert them to discrete time + Fx = np.identity(self.conf.nb_state-1) + self.conf.dt * Fx + Fu *= self.conf.dt + + return Fx, Fu + + def simulate_batch(self, state, action): + ''' Simulate dynamics using tensors and compute its gradient w.r.t control. Batch-wise computation ''' + state_next = np.array([self.simulate(s, a) for s, a in zip(state, action)]) + + return tf.convert_to_tensor(state_next, dtype=tf.float32) + + def derivative_batch(self, state, action): + ''' Simulate dynamics using tensors and compute its gradient w.r.t control. Batch-wise computation ''' + Fu = np.array([self.derivative(s, a) for s, a in zip(state, action)]) + + return tf.convert_to_tensor(Fu, dtype=tf.float32) + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + q = state[:self.nq] + + RF = self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id) + + H = self.conf.robot.framePlacement(q, RF, recompute) + + p = H.translation + + return p + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + +class SingleIntegrator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[0,0] = self.conf.dt + Fu[1,1] = self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0], + [0, 1]]) + + Fu[0,0] = self.conf.dt + Fu[1,1] = self.conf.dt + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*action[0] + state_next[1] = state[1] + self.conf.dt*action[1] + state_next[2] = state[2] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + + return p + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class DoubleIntegrator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = [self.get_end_effector_position(state)[i] for i in range(2)] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = np.math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class Car(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[2,0] = self.conf.dt + Fu[4,1] = self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0, -self.conf.dt*state[3]*math.sin(state[2]) -self.conf.dt**2*state[4]*math.sin(state[2])/2, self.conf.dt*math.cos(state[2]), self.conf.dt**2*math.cos(state[2])/2], + [0, 1, self.conf.dt*state[3]*math.cos(state[2]) +self.conf.dt**2*state[4]*math.cos(state[2])/2, self.conf.dt*math.sin(state[2]), self.conf.dt**2*math.sin(state[2])/2], + [0, 0, 1, 0, 0], + [0, 0, 0, 1, self.conf.dt], + [0, 0, 0, 0, 1]]) + + Fu[2,0] = self.conf.dt + Fu[4,1] = self.conf.dt + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*state[3]*tf.cos(state[2]) + self.conf.dt**2*state[4]*tf.cos(state[2])/2 + state_next[1] = state[1] + self.conf.dt*state[3]*tf.sin(state[2]) + self.conf.dt**2*state[4]*tf.sin(state[2])/2 + state_next[2] = state[2] + self.conf.dt*action[0] + state_next[3] = state[3] + self.conf.dt*state[4] + state_next[4] = state[4] + self.conf.dt*action[1] + state_next[5] = state[5] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + + return p + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class CarPark(Car): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def check_ICS_feasible(self, state): + ''' Check if ICS is not feasible ''' + # check if ee is in the obstacles + x_ee, y_ee = self.get_end_effector_position(state)[:2] + theta_ee = state[2] + + for i in range(len(self.conf.check_points_BF)): + check_points_WF_i = np.array([x_ee, y_ee]) + np.array([[math.cos(theta_ee), -math.sin(theta_ee)], [math.sin(theta_ee), math.cos(theta_ee)]]).dot(self.conf.check_points_BF[i,:]) + obs_1 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC1,self.YC1,self.A1,self.B1) + obs_2 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC2,self.YC2,self.A2,self.B2) + obs_3 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC3,self.YC3,self.A3,self.B3) + + feasible_flag = obs_1 < 0.5 and obs_2 < 0.5 and obs_3 < 0.5 + if feasible_flag == 0: + return feasible_flag + + return feasible_flag + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[3,0] = self.conf.dt + Fu[4,1] = self.conf.dt/self.conf.tau_delta + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0, -self.conf.dt*state[3]*math.sin(state[2]), self.conf.dt*math.cos(state[2]), 0], + [0, 1, self.conf.dt*state[3]*math.cos(state[2]), self.conf.dt*math.sin(state[2]), 0], + [0, 0, 1, self.conf.dt*math.tan(state[4])/self.conf.L_delta, self.conf.dt*state[3]*mpmath.sec(state[4])**2/self.conf.L_delta], + [0, 0, 0, 1, 0], + [0, 0, 0, 0, 1]]) + + Fu[3,0] = self.conf.dt + Fu[4,1] = self.conf.dt/self.conf.tau_delta + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*state[3]*math.cos(state[2]) + state_next[1] = state[1] + self.conf.dt*state[3]*math.sin(state[2]) + state_next[2] = state[2] + self.conf.dt*state[3]*math.tan(state[4])/self.conf.L_delta + state_next[3] = state[3] + self.conf.dt*action[0] + state_next[4] = state[4] + self.conf.dt*action[1]/self.conf.tau_delta + state_next[5] = state[5] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + np.array([[math.cos(state[2]), -math.sin(state[2])], [math.sin(state[2]), math.cos(state[2])]]).dot(np.array([self.conf.L_delta/2,0])) + + return p + + def obs_cost_fun(self,x,y,x_step,y_step,Wx,Wy,fv=1,k=50): + k = self.conf.k_db + + term1 = 4 + 4 * (y - y_step + Wy/2)**2 * k**2 + term2 = 4 + 4 * (y - y_step - Wy/2)**2 * k**2 + term3 = 4 + 4 * (x - x_step + Wx/2)**2 * k**2 + term4 = 4 + 4 * (x - x_step - Wx/2)**2 * k**2 + obs_cost = (term1)**(-1/2) * fv * (-np.sqrt(term2) / 2 + (y - y_step - Wy/2) * k) * (term3)**(-1/2) * (term2)**(-1/2) * (np.sqrt(term1) / 2 + (y - y_step + Wy/2) * k) * (term4)**(-1/2) * (np.sqrt(term3) / 2 + (x - x_step + Wx/2) * k) * (-np.sqrt(term4) / 2 + (x - x_step - Wx/2) * k) + + return obs_cost + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + theta_ee = state[2] + + obs_cost = 0 + check_points_WF = np.dot(np.array([[np.cos(theta_ee), -np.sin(theta_ee)], [np.sin(theta_ee), np.cos(theta_ee)]]), self.conf.check_points_BF.T).T + np.array([x_ee, y_ee]) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC1, self.YC1, self.A1, self.B1)) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC2, self.YC2, self.A2, self.B2)) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC3, self.YC3, self.A3, self.B3)) + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 + 0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 + 0.1) - math.sqrt(0.1) - 0.1 )) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*state[3]**2 - weights[3]*obs_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class Manipulator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = [self.get_end_effector_position(state)[i] for i in range(2)] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term penalizing the FINAL joint velocity + if weights[2] != 0: + vel_cost = state[self.nq:self.nx].dot(state[self.nq:self.nx]) + else: + vel_cost = 0 + + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*vel_cost - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) #- weights[2]*vel_cost + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class UR5(Env): + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.ZC1 = self.conf.obs_param[2] + self.XC2 = self.conf.obs_param[3] + self.YC2 = self.conf.obs_param[4] + self.ZC2 = self.conf.obs_param[5] + self.XC3 = self.conf.obs_param[6] + self.YC3 = self.conf.obs_param[7] + self.ZC3 = self.conf.obs_param[8] + + self.A1 = self.conf.obs_param[9] + self.B1 = self.conf.obs_param[10] + self.C1 = self.conf.obs_param[11] + self.A2 = self.conf.obs_param[12] + self.B2 = self.conf.obs_param[13] + self.C2 = self.conf.obs_param[14] + self.A3 = self.conf.obs_param[15] + self.B3 = self.conf.obs_param[16] + self.C3 = self.conf.obs_param[17] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee, z_ee = self.get_end_effector_position(state) + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) + ((z_ee-self.ZC1)**2)/((self.C1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) + ((z_ee-self.ZC2)**2)/((self.C2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) + ((z_ee-self.ZC3)**2)/((self.C3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((z_ee-self.TARGET_STATE[2])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + if action is not None: + u_cost = action.dot(action) + else: + u_cost = 0 + + # Term penalizing the FINAL joint velocity + vel_cost = state[self.nq:self.nx].dot(state[self.nq:self.nx]) + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + (z_ee-self.TARGET_STATE[2])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*vel_cost - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) diff --git a/Results Single Integrator/Results set test/Code/N_try_1/environment_TO.py b/Results Single Integrator/Results set test/Code/N_try_1/environment_TO.py new file mode 100644 index 0000000..0cf4b63 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/environment_TO.py @@ -0,0 +1,778 @@ +import sys +import math +import casadi +import numpy as np +import pinocchio.casadi as cpin + +class SingleIntegrator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + p_ee[-1] = 0 + + return p_ee + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*u[0] + x_next[1] = x[1] + self.conf.dt*u[1] + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class DoubleIntegrator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class Car_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + p_ee[-1] = 0 + + return p_ee + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*x[3]*casadi.cos(x[2]) + self.conf.dt**2*x[4]*casadi.cos(x[2])/2 + x_next[1] = x[1] + self.conf.dt*x[3]*casadi.sin(x[2]) + self.conf.dt**2*x[4]*casadi.sin(x[2])/2 + x_next[2] = x[2] + self.conf.dt*u[0] + x_next[3] = x[3] + self.conf.dt*x[4] + x_next[4] = x[4] + self.conf.dt*u[1] + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class CarPark_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + np.array([[np.cos(cx[2]), -np.sin(cx[2])], [np.sin(cx[2]), np.cos(cx[2])]]).dot(np.array([self.conf.L_delta/2,0])) + p_ee[-1] = 0 + + return p_ee + + def obs_cost_fun(self, x, y, x_clip, y_clip, Wx, Wy, fv=1, k=50): + k = self.conf.k_db + obs_cost = (4 + 4 * (y - y_clip + Wy / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * fv * (-np.sqrt(4 + 4 * (y - y_clip - Wy / 2) ** 2 * k ** 2) / 2 + (y - y_clip - Wy / 2) * k) * (4 + 4 * (x - x_clip + Wx / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (4 + 4 * (y - y_clip - Wy / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (np.sqrt(4 + 4 * (y - y_clip + Wy / 2) ** 2 * k ** 2) / 2 + (y - y_clip + Wy / 2) * k) * (4 + 4 * (x - x_clip - Wx / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (np.sqrt(4 + 4 * (x - x_clip + Wx / 2) ** 2 * k ** 2) / 2 + (x - x_clip + Wx / 2) * k) * (-np.sqrt(4 + 4 * (x - x_clip - Wx / 2) ** 2 * k ** 2) / 2 + (x - x_clip - Wx / 2) * k) + + return obs_cost + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def rotation_matrix(self, angle): + ''' Compute the 2x2 rotation matrix for a given angle ''' + cos_theta = casadi.cos(angle) + sin_theta = casadi.sin(angle) + + rotation_mat = casadi.vertcat(casadi.horzcat(cos_theta, -sin_theta), + casadi.horzcat(sin_theta, cos_theta)) + return rotation_mat + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee_tmp = self.p_ee(x) + p_ee = p_ee_tmp[:2] + theta_ee = x[2] + + ### Penalties representing the obstacle ### + obs_cost = 0 + check_points_WF = casadi.mtimes(self.rotation_matrix(theta_ee), self.conf.check_points_BF.T).T + casadi.repmat(p_ee.T, self.conf.check_points_BF.shape[0], 1) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC1, self.YC1, self.A1, self.B1)) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC2, self.YC2, self.A2, self.B2)) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC3, self.YC3, self.A3, self.B3)) + + ### Control effort term ### + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 2*0.1 - 2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*x[3]**2 + self.weights[3]*obs_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*x[3]*casadi.cos(x[2]) + x_next[1] = x[1] + self.conf.dt*x[3]*casadi.sin(x[2]) + x_next[2] = x[2] + self.conf.dt*x[3]*casadi.tan(x[4])/self.conf.L_delta + x_next[3] = x[3] + self.conf.dt*u[0] + x_next[4] = x[4] + self.conf.dt*u[1]/self.conf.tau_delta + + return x_next + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class Manipulator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class UR5_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.ZC1 = self.conf.obs_param[2] + self.XC2 = self.conf.obs_param[3] + self.YC2 = self.conf.obs_param[4] + self.ZC2 = self.conf.obs_param[5] + self.XC3 = self.conf.obs_param[6] + self.YC3 = self.conf.obs_param[7] + self.ZC3 = self.conf.obs_param[8] + + self.A1 = self.conf.obs_param[9] + self.B1 = self.conf.obs_param[10] + self.C1 = self.conf.obs_param[11] + self.A2 = self.conf.obs_param[12] + self.B2 = self.conf.obs_param[13] + self.C2 = self.conf.obs_param[14] + self.A3 = self.conf.obs_param[15] + self.B3 = self.conf.obs_param[16] + self.C3 = self.conf.obs_param[17] + + self.TARGET_STATE = self.conf.TARGET_STATE + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC1)**2)/((self.conf.A1/2)**2) + ((p_ee[1]-self.conf.YC1)**2)/((self.conf.B1/2)**2) + ((p_ee[2]-self.conf.ZC1)**2)/((self.conf.C1/2)**2) - 1.0)) + 1)/self.conf.alpha + ell2_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC2)**2)/((self.conf.A2/2)**2) + ((p_ee[1]-self.conf.YC2)**2)/((self.conf.B2/2)**2) + ((p_ee[2]-self.conf.ZC2)**2)/((self.conf.C2/2)**2) - 1.0)) + 1)/self.conf.alpha + ell3_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC3)**2)/((self.conf.A3/2)**2) + ((p_ee[1]-self.conf.YC3)**2)/((self.conf.B3/2)**2) + ((p_ee[2]-self.conf.ZC3)**2)/((self.conf.C3/2)**2) - 1.0)) + 1)/self.conf.alpha + + ### Control effort term ### + u_cost = 0 + for i in range(self.nu): + u_cost += u[i]**2 + self.conf.w_b*(u[i]/self.conf.u_max[i])**10 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.conf.alpha2*-(np.sqrt((p_ee[0]-self.conf.TARGET_STATE[0])**2 +0.1) - np.sqrt(0.1) - 0.1 + np.sqrt((p_ee[1]-self.conf.TARGET_STATE[1])**2 +0.1) - np.sqrt(0.1) - 0.1 + np.sqrt((p_ee[2]-self.conf.TARGET_STATE[2])**2 +0.1) - np.sqrt(0.1) - 0.1)) + 1)/self.conf.alpha2 + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.conf.TARGET_STATE[0])**2 + (p_ee[1]-self.conf.TARGET_STATE[1])**2 + (p_ee[2]-self.conf.TARGET_STATE[2])**2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_1/generate_tests_set_script.py b/Results Single Integrator/Results set test/Code/N_try_1/generate_tests_set_script.py new file mode 100644 index 0000000..4c5f781 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/generate_tests_set_script.py @@ -0,0 +1,26 @@ +### Create file to be executed ### +import os +import shutil + +system_id = 'car' +system_id_short = 'C' + +n_runs = 10 + +w_S = 1e-3 +info = '{}'.format(1) + +offset = 0 if w_S ==0 else 100 + +seed_list = [29556, 5280, 739, 92, 10, 7298, 14, 264, 22135, 342] + +shutil.copy('Template.sh', '{}{}.sh'.format(system_id_short,info)) +with open('{}{}.sh'.format(system_id_short,info), 'a') as f: + for i in range(1,1 + n_runs): + f.write("nohup python3 -u main.py --system-id='{}' --test-n={} --seed={} --w-S={} > out/{}{}.txt &\n".format(system_id,offset+i,seed_list[i-1], w_S, system_id_short,offset+i)) + f.write("\necho 'Running scripts in parallel'\n") + f.write("echo 'PID of this script is:' $$\n") + f.write("wait\n") + f.write("echo 'Script done running'\n") + +os.makedirs('out', exist_ok=True) \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_1/main.py b/Results Single Integrator/Results set test/Code/N_try_1/main.py new file mode 100644 index 0000000..8121e8c --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/main.py @@ -0,0 +1,254 @@ +import os +import sys +import time +import shutil +import random +import argparse +import importlib +import numpy as np +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # {'0' -> show all logs, '1' -> filter out info, '2' -> filter out warnings} +import tensorflow as tf +from multiprocessing import Pool +from RL import RL_AC +from TO import TO_Casadi +from plot_utils import PLOT +from NeuralNetwork import NN +from replay_buffer import PrioritizedReplayBuffer, ReplayBuffer + +def parse_args(): + ''' Parse the arguments for CACTO training ''' + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + + parser.add_argument('--test-n', type=int, default=0, + help="Test number") + + parser.add_argument('--seed', type=int, default=0, + help="random and tf.random seed") + + parser.add_argument('--system-id', type=str, default='single_integrator', + choices=["single_integrator", "double_integrator", "car", "car_park", "manipulator", "ur5"], + help="System-id (single_integrator, double_integrator, car, manipulator, ur5") + + parser.add_argument('--recover-training-flag', type=bool, default=False, + choices=["True", "False"], + help="Flag to recover training") + ### Not tested ### + #parser.add_argument('--GPU-flag', type=bool, default=False, + # choices=["True", "False"], + # help="Flag to use GPU") + + parser.add_argument('--nb-cpus', type=int, default=10, + help="Number of TO problems solved in parallel") + + parser.add_argument('--w-S', type=float, default=0, + help="Sobolev training - weight of the value related error") + + args = parser.parse_args() + dict_args = vars(args) + + return dict_args + + + + +if __name__ == '__main__': + + args = parse_args() + + ############### Input ############### + N_try = args['test_n'] + + if args['seed'] == None: + seed = random.randint(1,100000) + else: + seed = args['seed'] + tf.random.set_seed(seed) # Set tensorflow seed + random.seed(seed) # Set random seed + + system_id = args['system_id'] + + recover_training_flag = args['recover_training_flag'] + + ### Not tested ### + #GPU_flag = args['GPU_flag'] + #if GPU_flag: + # os.environ["CUDA_VISIBLE_DEVICES"]="-1" + #print(tf.config.experimental.list_physical_devices('GPU')) + + nb_cpus = args['nb_cpus'] + + w_S = args['w_S'] + ######################################################### + + + + # Import configuration file and environment file + system_map = { + 'single_integrator': ('conf_single_integrator', 'SingleIntegrator', 'SingleIntegrator_CAMS'), + 'double_integrator': ('conf_double_integrator', 'DoubleIntegrator', 'DoubleIntegrator_CAMS'), + 'car': ('conf_car', 'Car', 'Car_CAMS'), + 'car_park': ('conf_car_park', 'CarPark', 'CarPark_CAMS'), + 'manipulator': ('conf_manipulator', 'Manipulator', 'Manipulator_CAMS'), + 'ur5': ('conf_ur5', 'UR5', 'UR5_CAMS') + } + try: + conf_module, env_class, env_TO_class = system_map[system_id] + conf = importlib.import_module(conf_module) + Environment = getattr(importlib.import_module('environment'), env_class) + Environment_TO = getattr(importlib.import_module('environment_TO'), env_TO_class) + except KeyError: + print('System {} not found'.format(system_id)) + sys.exit() + + # Create folders to store the results and the trained NNs + for path in conf.path_list: + os.makedirs(path + '/N_try_{}'.format(N_try), exist_ok=True) + os.makedirs(conf.Config_path, exist_ok=True) + + # Save configuration + params = [p for p in conf.__dict__ if not p.startswith("__")] + with open(conf.Config_path + '/config{}.txt'.format(N_try), 'w') as f: + for p in params: + f.write('{} = {}\n'.format(p, conf.__dict__[p])) + f.write('Seed = {}\n'.format(seed)) + f.write('w_S = {}'.format(w_S)) + + shutil.copy('{}.py'.format(conf_module), conf.Config_path + '/' + conf_module + '_{}.py'.format(N_try)) + with open(conf.Config_path + '/' + conf_module + '_{}.py'.format(N_try), 'a') as f: + f.write('\n\n# {}'.format(args)) + + # Copy all file with .py extension from /mydir to /mydestdir + for file in os.listdir("./"): + if file.endswith(".py"): + shutil.copy(os.path.join("./", file), os.path.join(conf.Code_path + '/N_try_{}'.format(N_try), file)) + + # Create empty txt file in Log_path to store the test info + open(conf.Log_path + '/info.txt', 'a').close() + + + + ### Create instances of the used classes ### + env = Environment(conf) # Create environment instances + env_TO = Environment_TO + NN_inst = NN(env, conf, w_S) # Create NN instance + TrOp = TO_Casadi(env, conf, env_TO, w_S) # Create TO instance + RLAC = RL_AC(env, NN_inst, conf, N_try) # Create RL instance + buffer = ReplayBuffer(conf) if conf.prioritized_replay_alpha == 0 else PrioritizedReplayBuffer(conf) # Create an empty (prioritized) replay buffer + plot_fun = PLOT(N_try, env, NN_inst, conf) # Create PLOT instance + + # Set initial weights of the NNs, initialize the counter of the updates and setup NN models + if recover_training_flag: + recover_training = np.array([conf.NNs_path_rec, conf.N_try_rec, conf.update_step_counter_rec]) + update_step_counter = conf.update_step_counter_rec + + RLAC.setup_model(recover_training) + else: + update_step_counter = 0 + + RLAC.setup_model() + + # Save initial weights of the NNs + RLAC.RL_save_weights(update_step_counter) + + # Plot initial rollouts + plot_fun.plot_traj_from_ICS(np.array(conf.init_states_sim), TrOp, RLAC, update_step_counter=update_step_counter,steps=conf.NSTEPS, init=0) + + # Initialize arrays to store the reward history of each episode and the average reward history of last 100 episodes + ep_arr_idx = 0 + ep_reward_arr = np.zeros(conf.NEPISODES-ep_arr_idx)*np.nan + + def compute_sample(args): + ''' Create samples solving TO problems starting from given ICS ''' + ep = args[0] + ICS = args[1] + + # Create initial TO # + init_rand_state, init_TO_states, init_TO_controls, NSTEPS_SH, success_init_flag = RLAC.create_TO_init(ep, ICS) + if success_init_flag == 0: + return None + + # Solve TO problem # + TO_controls, TO_states, success_flag, TO_ee_pos_arr, TO_step_cost, dVdx = TrOp.TO_Solve(init_rand_state, init_TO_states, init_TO_controls, NSTEPS_SH) + if success_flag == 0: + return None + + # Collect experiences + state_arr, partial_reward_to_go_arr, total_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, RL_ee_pos_arr = RLAC.RL_Solve(TO_controls, TO_states, TO_step_cost) + + if conf.env_RL == 0: + RL_ee_pos_arr = TO_ee_pos_arr + + return NSTEPS_SH, TO_controls, TO_ee_pos_arr, dVdx, state_arr.tolist(), partial_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, RL_ee_pos_arr + + def create_unif_TO_init(n_UICS=1): + ''' Create n uniformely distributed ICS ''' + # Create ICS TO # + init_rand_state = env.reset() + + return init_rand_state + + + + ### START TRAINING ### + if conf.profile: + import cProfile, pstats + + profiler = cProfile.Profile() + profiler.enable() + + time_start = time.time() + + for ep in range(conf.NLOOPS): + # Generate and store conf.EP_UPDATE random-uniform ICS + with Pool(nb_cpus) as p: + init_rand_state = p.map(create_unif_TO_init, range(conf.EP_UPDATE)) + + # Generate samples + with Pool(nb_cpus) as p: + tmp = p.map(compute_sample, zip(ep*np.ones(conf.EP_UPDATE), init_rand_state)) + + # Remove unsuccessful TO problems and update EP_UPDATE + tmp = [x for x in tmp if x is not None] + NSTEPS_SH, TO_controls, ee_pos_arr_TO, dVdx, state_arr, partial_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, ee_pos_arr_RL = zip(*tmp) + + # Update the buffer + buffer.add(state_arr, partial_reward_to_go_arr, state_next_rollout_arr, dVdx, done_arr, term_arr) + + # Update NNs + update_step_counter = RLAC.learn_and_update(update_step_counter, buffer, ep) + + # plot Critic value function + #plot_fun.plot_Critic_Value_function(RLAC.critic_model, update_step_counter, system_id) ### + + # Plot rollouts and state and control trajectories + if update_step_counter%conf.plot_rollout_interval_diff_loc == 0 or system_id == 'single_integrator' or system_id == 'double_integrator' or system_id == 'car_park' or system_id == 'car' or system_id == 'manipulator': + print("System: {} - N_try = {}".format(conf.system_id, N_try)) + plot_fun.plot_Critic_Value_function(RLAC.critic_model, update_step_counter, system_id) + plot_fun.plot_traj_from_ICS(np.array(conf.init_states_sim), TrOp, RLAC, update_step_counter=update_step_counter, ep=ep,steps=conf.NSTEPS, init=1) + + # Update arrays to store the reward history and its average + ep_reward_arr[ep_arr_idx:ep_arr_idx+len(tmp)] = ep_return + ep_arr_idx += len(tmp) + + for i in range(len(tmp)): + print("Episode {} ---> Return = {}".format(ep*len(tmp) + i, ep_return[i])) + + if update_step_counter > conf.NUPDATES: + break + + time_end = time.time() + print('Elapsed time: ', time_end-time_start) + + if conf.profile: + profiler.disable() + stats = pstats.Stats(profiler).sort_stats('cumtime') + stats.print_stats() + + # Plot returns + plot_fun.plot_Return(ep_reward_arr) + + # Save networks at the end of the training + RLAC.RL_save_weights() + + # Simulate the final policy + plot_fun.rollout(update_step_counter, RLAC.actor_model, conf.init_states_sim) diff --git a/Results Single Integrator/Results set test/Code/N_try_1/plot_utils.py b/Results Single Integrator/Results set test/Code/N_try_1/plot_utils.py new file mode 100644 index 0000000..55daab0 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/plot_utils.py @@ -0,0 +1,714 @@ +import math +import numpy as np +import tensorflow as tf +import matplotlib.pyplot as plt +from matplotlib import cm, colors +from matplotlib.patches import Ellipse, FancyBboxPatch, Rectangle +from matplotlib.transforms import Affine2D +import mpl_toolkits.mplot3d.art3d as art3d + +class PLOT(): + def __init__(self, N_try, env, NN, conf): + ''' + :input N_try : (Test number) + + :input env : (Environment instance) + + :input conf : (Configuration file) + :param fig_ax_lim : (float array) Figure axis limit [x_min, x_max, y_min, y_max] + :param Fig_path : (str) Figure path + :param NSTEPS : (int) Max episode length + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param NORMALIZE_INPUTS : (bool) Flag to normalize inputs (state) + :param state_norm_array : (float array) Array used to normalize states + :param dt : (float) Timestep + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + ''' + self.env = env + self.NN = NN + self.conf = conf + + self.N_try = N_try + + self.xlim = conf.fig_ax_lim[0].tolist() + self.ylim = conf.fig_ax_lim[1].tolist() + + # Set the ticklabel font size globally + plt.rcParams['xtick.labelsize'] = 22 + plt.rcParams['ytick.labelsize'] = 22 + plt.rcParams.update({'font.size': 20}) + + return + + def plot_obstaces(self, a=1): + if self.conf.system_id == 'car_park': + obs1 = Rectangle((self.conf.XC1-self.conf.A1/2, self.conf.YC1-self.conf.B1/2), self.conf.A1, self.conf.B1, 0.0,alpha=a) + obs1.set_facecolor([30/255, 130/255, 76/255, 1]) + obs2 = Rectangle((self.conf.XC2-self.conf.A2/2, self.conf.YC2-self.conf.B2/2), self.conf.A2, self.conf.B2, 0.0,alpha=a) + obs2.set_facecolor([30/255, 130/255, 76/255, 1]) + obs3 = Rectangle((self.conf.XC3-self.conf.A3/2, self.conf.YC3-self.conf.B3/2), self.conf.A3, self.conf.B3, 0.0,alpha=a) + obs3.set_facecolor([30/255, 130/255, 76/255, 1]) + + #rec1 = FancyBboxPatch((self.conf.XC1-self.conf.A1/2, self.conf.YC1-self.conf.B1/2), self.conf.A1, self.conf.B1,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec1.set_facecolor([30/255, 130/255, 76/255, 1]) + #rec2 = FancyBboxPatch((self.conf.XC2-self.conf.A2/2, self.conf.YC2-self.conf.B2/2), self.conf.A2, self.conf.B2,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec2.set_facecolor([30/255, 130/255, 76/255, 1]) + #rec3 = FancyBboxPatch((self.conf.XC3-self.conf.A3/2, self.conf.YC3-self.conf.B3/2), self.conf.A3, self.conf.B3,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec3.set_facecolor([30/255, 130/255, 76/255, 1]) + else: + obs1 = Ellipse((self.conf.XC1, self.conf.YC1), self.conf.A1, self.conf.B1, 0.0,alpha=a) + obs1.set_facecolor([30/255, 130/255, 76/255, 1]) + obs2 = Ellipse((self.conf.XC2, self.conf.YC2), self.conf.A2, self.conf.B2, 0.0,alpha=a) + obs2.set_facecolor([30/255, 130/255, 76/255, 1]) + obs3 = Ellipse((self.conf.XC3, self.conf.YC3), self.conf.A3, self.conf.B3, 0.0,alpha=a) + obs3.set_facecolor([30/255, 130/255, 76/255, 1]) + + return [obs1, obs2, obs3] + + def plot_Reward(self, plot_obs=0): + x = np.arange(-15, 15, 0.1) + y = np.arange(-10, 10, 0.1) + theta = np.pi/2 + ICS = np.array([np.array([i,j,0]) for i in x for j in y]) + state = np.array([self.compute_ICS(np.array([i,j,0]), 'car')[0] for i in x for j in y]) # for k in theta] + state[:,2] = theta + r = [self.env.reward(self.conf.cost_weights_running, s) for s in state] + mi = min(r) + ma = max(r) + norm = colors.Normalize(vmin=mi,vmax=ma) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + pti = ax.scatter(ICS[:,0], ICS[:,1], norm=norm, c=r, cmap=cm.get_cmap('hot_r')) + plt.colorbar(pti) + + if plot_obs: + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + + # Center and check points of 'car_park' system + #check_points_WF_i = np.array([[math.cos(theta), -math.sin(theta)], [math.sin(theta), math.cos(theta)]]).dot(self.conf.check_points_BF[0,:]) + ICS[0,:2] + #ax.scatter(check_points_WF_i[0], check_points_WF_i[1], c='b') + #for i in range(1,len(self.conf.check_points_BF)): + # check_points_WF_i = np.array([[math.cos(theta), -math.sin(theta)], [math.sin(theta), math.cos(theta)]]).dot(self.conf.check_points_BF[i,:]) + ICS[0,:2] + # ax.scatter(check_points_WF_i[0], check_points_WF_i[1], c='r') + + ax.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5, legend='Goal position') + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.set_title('Plane') + #ax.legend() + ax.grid(True) + plt.show() + + def compute_ICS(self, p_ee, sys_id, theta=None, continue_flag=0): + if sys_id == 'manipulator': + radius = math.sqrt((p_ee[0]-self.conf.x_base)**2+(p_ee[1])**2) + if radius > 30: + continue_flag = 1 + return None, continue_flag + + phi = math.atan2(p_ee[1]-self.conf.y_base,(p_ee[0]-self.conf.x_base)) # SUM OF THE ANGLES FIXED + X3rd_joint = (p_ee[0]-self.conf.x_base) - self.conf.l* math.cos(phi) + Y3rd_joint = (p_ee[1]-self.conf.y_base) - self.conf.l* math.sin(phi) + + if abs(X3rd_joint) <= 1e-6 and abs(Y3rd_joint) <= 1e-6: + continue_flag = 1 + return None, continue_flag + + c2 = (X3rd_joint**2 + Y3rd_joint**2 -2*self.conf.l**2)/(2*self.conf.l**2) + + if p_ee[1] >= 0: + s2 = math.sqrt(1-c2**2) + else: + s2 = -math.sqrt(1-c2**2) + + s1 = ((self.conf.l + self.conf.l*c2)*Y3rd_joint - self.conf.l*s2*X3rd_joint)/(X3rd_joint**2 + Y3rd_joint**2) + c1 = ((self.conf.l + self.conf.l*c2)*X3rd_joint - self.conf.l*s2*Y3rd_joint)/(X3rd_joint**2 + Y3rd_joint**2) + ICS_q0 = math.atan2(s1,c1) + ICS_q1 = math.atan2(s2,c2) + ICS_q2 = phi-ICS_q0-ICS_q1 + + ICS = np.array([ICS_q0, ICS_q1, ICS_q2, 0.0, 0.0, 0.0, 0.0]) + + elif sys_id == 'car': + if theta == None: + theta = 0*np.random.uniform(-math.pi,math.pi) + ICS = np.array([p_ee[0], p_ee[1], theta, 0.0, 0.0, 0.0]) + + elif sys_id == 'car_park': + if theta == None: + #theta = 0*np.random.uniform(-math.pi,math.pi) + theta = np.pi/2 + ICS = np.array([p_ee[0], p_ee[1], theta, 0.0, 0.0, 0.0]) + + elif sys_id == 'double_integrator': + ICS = np.array([p_ee[0], p_ee[1], 0.0, 0.0, 0.0]) + + elif sys_id == 'single_integrator': + ICS = np.array([p_ee[0], p_ee[1], 0.0]) + + return ICS, continue_flag + + def plot_policy(self, tau, x, y, steps, n_updates, diff_loc=0): + ''' Plot policy rollout from a single initial state as well as state and control trajectories ''' + timesteps = self.self.conf.dt*np.arange(steps) + + fig = plt.figure(figsize=(12,8)) + plt.suptitle('POLICY: Discrete model, N try = {} N updates = {}'.format(self.N_try,n_updates), y=1) + + ax1 = fig.add_subplot(2, 2, 1) + ax1.plot(timesteps, x, 'ro', linewidth=1, markersize=1, legedn='x') + ax1.plot(timesteps, y, 'bo', linewidth=1, markersize=1, legend='y') + ax1.set_xlabel('Time [s]') + ax1.set_ylabel('[m]') + ax1.set_title('End-Effector Position') + ax1.legend() + ax1.grid(True) + + col = ['ro', 'bo', 'go'] + ax2 = fig.add_subplot(2, 2, self.conf.nb_action) + for i in range(self.conf.nb_action): + ax2.plot(timesteps, tau[:,i], col[i], linewidth=1, markersize=1,legend='tau{}'.format(i)) + ax2.set_xlabel('Time [s]') + ax2.set_title('Controls') + ax2.legend() + ax2.grid(True) + + ax3 = fig.add_subplot(1, 2, 2) + ax3.plot(x, y, 'ro', linewidth=1, markersize=1) + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax3.add_artist(obs_plot_list[i]) + ax3.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=10) + ax3.set_xlim(self.xlim) + ax3.set_ylim(self.ylim) + ax3.set_aspect('equal', 'box') + ax3.set_xlabel('X [m]') + ax3.set_ylabel('Y [m]') + ax3.set_title('Plane') + ax3.grid(True) + + fig.tight_layout() + + if diff_loc==0: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationSingleInit_{}_{}'.format(self.N_try,n_updates)) + else: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationMultiInit_{}_{}'.format(self.N_try,n_updates)) + + plt.clf() + plt.close(fig) + + def plot_policy_eval(self, p_list, n_updates, diff_loc=0, theta=0): + ''' Plot only policy rollouts from multiple initial states ''' + fig = plt.figure(figsize=(12,8)) + plt.suptitle('POLICY: Discrete model, N try = {} N updates = {}'.format(self.N_try,n_updates), y=1) + + ax = fig.add_subplot(1, 1, 1) + for idx in range(len(p_list)): + ax.plot(p_list[idx][:,0], p_list[idx][:,1], marker='o', linewidth=0.3, markersize=1) + ax.plot(p_list[idx][0,0],p_list[idx][0,1],'ko',markersize=5) + if self.conf.system_id == 'car_park': + theta = p_list[idx][-1,2] + fancybox = FancyBboxPatch((0 - self.conf.L/2, 0 - self.conf.W/2), self.conf.L, self.conf.W, edgecolor='none', alpha=0.5, boxstyle='round,pad=0') + fancybox.set_transform(Affine2D().rotate_deg(np.rad2deg(theta)).translate(p_list[idx][-1,0], p_list[idx][-1,1]) + ax.transData) + ax.add_patch(fancybox) + + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + + ax.plot(self.conf.TARGET_STATE[0],self.conf.TARGET_STATE[1],'b*',markersize=10) + + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.grid(True) + fig.tight_layout() + if diff_loc==0: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationSingleInit_{}_{}'.format(self.N_try,n_updates)) + else: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationMultiInit_{}_{}'.format(self.N_try,n_updates)) + + plt.clf() + plt.close(fig) + + def rollout(self,update_step_cntr, actor_model, init_states_sim, diff_loc=0): + ''' Plot rollout of the actor from some initial states. It generates the results and then calls plot_policy() and plot_policy_eval() ''' + #tau_all_sim = [] + p_ee_all_sim = [] + + returns = {} + + for k in range(len(init_states_sim)): + rollout_controls = np.zeros((self.conf.NSTEPS,self.conf.nb_action)) + rollout_states = np.zeros((self.conf.NSTEPS+1,self.conf.nb_state)) + rollout_p_ee = np.zeros((self.conf.NSTEPS+1,3)) + rollout_episodic_reward = 0 + + rollout_p_ee[0,:] = self.env.get_end_effector_position(init_states_sim[k]) + rollout_states[0,:] = np.copy(init_states_sim[k]) + + for i in range(self.conf.NSTEPS): + rollout_controls[i,:] = tf.squeeze(self.NN.eval(actor_model, np.array([rollout_states[i,:]]))).numpy() + rollout_states[i+1,:], rwrd_sim = self.env.step(self.conf.cost_weights_running, rollout_states[i,:],rollout_controls[i,:]) + rollout_p_ee[i+1,:] = self.env.get_end_effector_position(rollout_states[i+1,:]) + + rollout_p_ee[i+1,-1] = rollout_states[i+1,2] ### !!! ### + + rollout_episodic_reward += rwrd_sim + + if k==0: + print("N try = {}: Simulation Return @ N updates = {} ==> {}".format(self.N_try,update_step_cntr,rollout_episodic_reward)) + + p_ee_all_sim.append(rollout_p_ee) + + returns[init_states_sim[k][0],init_states_sim[k][1]] = rollout_episodic_reward + + self.plot_policy_eval(p_ee_all_sim,update_step_cntr, diff_loc=diff_loc) + + return returns + + def plot_results(self, tau, ee_pos_TO, ee_pos_RL, steps, to=0): + ''' Plot results from TO and episode to check consistency ''' + timesteps = self.conf.dt*np.arange(steps+1) + fig = plt.figure(figsize=(12,8)) + if to: + plt.suptitle('TO EXPLORATION: N try = {}'.format(self.N_try), y=1, fontsize=20) + else: + plt.suptitle('POLICY EXPLORATION: N try = {}'.format(self.N_try), y=1, fontsize=20) + + ax1 = fig.add_subplot(2, 2, 1) + ax1.plot(timesteps, ee_pos_TO[:,0], 'ro', linewidth=1, markersize=1,legend="x_TO") + ax1.plot(timesteps, ee_pos_TO[:,1], 'bo', linewidth=1, markersize=1,legend="y_TO") + ax1.plot(timesteps, ee_pos_RL[:,0], 'go', linewidth=1, markersize=1,legend="x_RL") + ax1.plot(timesteps, ee_pos_RL[:,1], 'ko', linewidth=1, markersize=1,legend="y_RL") + ax1.set_xlabel('Time [s]') + ax1.set_ylabel('[m]') + ax1.set_title('End-Effector Position') + ax1.set_xlim(0, timesteps[-1]) + ax1.legend() + ax1.grid(True) + + ax2 = fig.add_subplot(2, 2, 3) + col = ['ro', 'bo', 'go'] + for i in range(self.conf.nb_action): + ax2.plot(timesteps[:-1], tau[:,i], col[i], linewidth=1, markersize=1,legend='tau{}'.format(i)) + ax2.set_xlabel('Time [s]') + ax2.set_title('Controls') + ax2.legend() + ax2.grid(True) + + ax3 = fig.add_subplot(1, 2, 2) + ax3.plot(ee_pos_TO[:,0], ee_pos_TO[:,1], 'ro', linewidth=1, markersize=2,legend='TO') + ax3.plot(ee_pos_RL[:,0], ee_pos_RL[:,1], 'bo', linewidth=1, markersize=2,legend='RL') + ax3.plot([ee_pos_TO[0,0]],[ee_pos_TO[0,1]],'ro',markersize=5) + ax3.plot([ee_pos_RL[0,0]],[ee_pos_RL[0,1]],'bo',markersize=5) + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax3.add_artist(obs_plot_list[i]) + ax3.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax3.set_xlim(self.xlim) + ax3.set_ylim(self.ylim) + ax3.set_aspect('equal', 'box') + ax3.set_xlabel('X [m]') + ax3.set_ylabel('Y [m]') + ax3.set_title('Plane') + ax3.legend() + ax3.grid(True) + + fig.tight_layout() + #plt.show() + + def plot_Return(self, ep_reward_list): + ''' Plot returns (not so meaningful given that the initial state, so also the time horizon, of each episode is randomized) ''' + fig = plt.figure(figsize=(15,8)) + ax = fig.add_subplot(1, 1, 1) + ax.set_yscale('log') + ax.plot(ep_reward_list**2) + ax.set_xlabel("Episode") + ax.set_ylabel("Return") + ax.set_title("N_try = {}".format(self.N_try)) + ax.grid(True) + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/EpReturn_{}'.format(self.N_try)) + plt.close() + + def plot_Critic_Value_function(self, critic_model, n_update, sys_id, name='V'): + ''' Plot Value function as learned by the critic ''' + if sys_id == 'manipulator': + N_discretization_x = 60 + 1 + N_discretization_y = 60 + 1 + + plot_data = np.zeros(N_discretization_y*N_discretization_x)*np.nan + ee_pos = np.zeros((N_discretization_y*N_discretization_x,3))*np.nan + + for k_x in range(N_discretization_x): + for k_y in range(N_discretization_y): + ICS = self.env.reset() + ICS[-1] = 0 + ee_pos[k_x*(N_discretization_y)+k_y,:] = self.env.get_end_effector_position(ICS) + plot_data[k_x*(N_discretization_y)+k_y] = self.NN.eval(critic_model, np.array([ICS])) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot() + plt.scatter(ee_pos[:,0], ee_pos[:,1], c=plot_data, cmap=cm.coolwarm, antialiased=False) + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + plt.colorbar() + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/{}_{}'.format(self.conf.Fig_path,self.N_try,name,int(n_update))) + plt.close() + + else: + N_discretization_x = 30 + 1 + N_discretization_y = 30 + 1 + + plot_data = np.zeros((N_discretization_y,N_discretization_x))*np.nan + + ee_x = np.linspace(-15, 15, N_discretization_x) + ee_y = np.linspace(-15, 15, N_discretization_y) + + for k_y in range(N_discretization_y): + for k_x in range(N_discretization_x): + p_ee = np.array([ee_x[k_x], ee_y[k_y], 0]) + ICS, continue_flag = self.compute_ICS(p_ee, sys_id, continue_flag=0) + if continue_flag: + continue + plot_data[k_x,k_y] = self.NN.eval(critic_model, np.array([ICS])) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot() + plt.contourf(ee_x, ee_y, plot_data.T, cmap=cm.coolwarm, antialiased=False) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + plt.colorbar() + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/{}_{}'.format(self.conf.Fig_path,self.N_try,name,int(n_update))) + plt.close() + + def plot_Critic_Value_function_from_sample(self, n_update, NSTEPS_SH, state_arr, reward_arr): + # Store transition after computing the (partial) cost-to go when using n-step TD (from 0 to Monte Carlo) + reward_to_go_arr = np.zeros(sum(NSTEPS_SH)+len(NSTEPS_SH)*1) + idx = 0 + for n in range(len(NSTEPS_SH)): + for i in range(NSTEPS_SH[n]+1): + # Compute the partial cost to go + reward_to_go_arr[idx] = sum(reward_arr[n][i:]) + idx += 1 + + state_arr = np.concatenate(state_arr, axis=0) + ee_pos_arr = np.zeros((len(state_arr),3)) + for i in range(state_arr.shape[0]): + ee_pos_arr[i,:] = self.env.get_end_effector_position(state_arr[i]) + + + mi = min(reward_to_go_arr) + ma = max(reward_to_go_arr) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot()#projection='3d') + norm = colors.Normalize(vmin=mi,vmax=ma) + + obs_plot_list = self.plot_obstaces(a=0.5) + + ax.scatter(ee_pos_arr[:,0],ee_pos_arr[:,1], c=reward_to_go_arr, norm=norm, cmap=cm.coolwarm, marker='x') + + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + + plt.colorbar(cm.ScalarMappable(norm=norm,cmap=cm.coolwarm)) + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/V_sample_{}'.format(self.conf.Fig_path,self.N_try,int(n_update))) + plt.close() + + def plot_ICS(self,state_arr): + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + for j in range(len(state_arr)): + ax.scatter(state_arr[j][0,0],state_arr[j][0,1]) + obs_plot_list = plot_fun.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.fig_ax_lim[0].tolist()) + ax.set_ylim(self.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/ICS_{}_S{}'.format(conf.Fig_path,N_try,update_step_counter,int(w_S))) + plt.close(fig) + + def plot_rollout_and_traj_from_ICS(self, init_state, n_update, actor_model, TrOp, tag, steps=200): + ''' Plot results from TO and episode to check consistency ''' + colors = cm.coolwarm(np.linspace(0.1,1,len(init_state))) + + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + + for j in range(len(init_state)): + + ee_pos_TO = np.zeros((steps,3)) + ee_pos_RL = np.zeros((steps,3)) + + RL_states = np.zeros((steps,self.conf.nb_state)) + RL_action = np.zeros((steps-1,self.conf.nb_action)) + RL_states[0,:] = init_state[j,:] + ee_pos_RL[0,:] = self.env.get_end_effector_position(RL_states[0,:]) + + for i in range(steps-1): + RL_action[i,:] = self.NN.eval(actor_model, np.array([RL_states[i,:]])) + RL_states[i+1,:] = self.env.simulate(RL_states[i,:], RL_action[i,:]) + ee_pos_RL[i+1,:] = self.env.get_end_effector_position(RL_states[i+1,:]) + + TO_states, _ = TrOp.TO_System_Solve3(init_state[j,:], RL_states.T, RL_action.T, steps-1) + + try: + for i in range(steps): + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[i,:]) + except: + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[0,:]) + + ax.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax.scatter(ee_pos_TO[0,0],ee_pos_TO[0,1],color=colors[j]) + ax.scatter(ee_pos_RL[0,0],ee_pos_RL[0,1],color=colors[j]) + ax.plot(ee_pos_TO[1:,0],ee_pos_TO[1:,1],color=colors[j]) + ax.plot(ee_pos_RL[1:,0],ee_pos_RL[1:,1],'--',color=colors[j]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.set_title('Plane') + #ax.legend() + ax.grid(True) + + plt.savefig('{}/N_try_{}/ee_traj_{}_{}'.format(self.conf.Fig_path,self.N_try,int(n_update), tag)) + + def plot_ICS(self, input_arr, cs=0): + if cs == 1: + p_arr = np.zeros((len(input_arr),3)) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + for j in range(len(input_arr)): + p_arr[j,:] = input_arr[j,:] + ax.scatter(p_arr[:,0],p_arr[:,1]) + obs_plot_list = self.plot_obstaces(a = 0.5) + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.conf.fig_ax_lim[0].tolist()) + ax.set_ylim(self.conf.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + ax.grid() + plt.savefig('{}/N_try_{}/ICS'.format(self.conf.Fig_path,self.N_try)) + plt.close(fig) + else: + p_arr = np.zeros((len(input_arr),3)) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + + for j in range(len(input_arr)): + p_arr[j,:] = self.env.get_end_effector_position(input_arr[j]) + ax.scatter(p_arr[:,0],p_arr[:,1]) + obs_plot_list = self.plot_obstaces(a = 0.5) + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.conf.fig_ax_lim[0].tolist()) + ax.set_ylim(self.conf.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + ax.grid() + plt.savefig('{}/N_try_{}/ICS'.format(self.conf.Fig_path,self.N_try)) + plt.close(fig) + + def plot_traj_from_ICS(self, init_state, TrOp, RLAC, update_step_counter=0,ep=0,steps=200, init=0,continue_flag=1): + ''' Plot results from TO and episode to check consistency ''' + colors = cm.coolwarm(np.linspace(0.1,1,len(init_state))) + + fig = plt.figure(figsize=(12,8)) + ax1 = fig.add_subplot(1,2,1) + ax2 = fig.add_subplot(1,2,2) + + for j in range(len(init_state)): + + ee_pos_TO = np.zeros((steps,3)) + ee_pos_RL = np.zeros((steps,3)) + + if init == 0: + # zeros + _, init_TO_states, init_TO_controls, _, success_init_flag = RLAC.create_TO_init(0, init_state[j,:]) + elif init == 1: + # NN + _, init_TO_states, init_TO_controls, _, success_init_flag = RLAC.create_TO_init(1, init_state[j,:]) + + if success_init_flag: + _, _, TO_states, _, _, _ = TrOp.TO_System_Solve(init_state[j,:], init_TO_states, init_TO_controls, steps-1) + else: + continue + + try: + for i in range(steps): + ee_pos_RL[i,:] = self.env.get_end_effector_position(init_TO_states[i,:]) + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[i,:]) + except: + ee_pos_RL[i,:] = self.env.get_end_effector_position(init_TO_states[0,:]) + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[0,:]) + + ax1.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax1.scatter(ee_pos_RL[0,0],ee_pos_RL[0,1],color=colors[j]) + ax1.plot(ee_pos_RL[1:,0],ee_pos_RL[1:,1],'--',color=colors[j]) + + ax2.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax2.scatter(ee_pos_TO[0,0],ee_pos_TO[0,1],color=colors[j]) + ax2.plot(ee_pos_TO[1:,0],ee_pos_TO[1:,1],color=colors[j]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax1.add_patch(obs_plot_list[i]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax2.add_patch(obs_plot_list[i]) + + ax1.set_xlim(self.xlim) + ax1.set_ylim(self.ylim) + ax1.set_aspect('equal', 'box') + ax1.set_xlabel('X [m]') + ax1.set_ylabel('Y [m]') + ax1.set_title('Warmstart traj.') + + ax2.set_xlim(self.xlim) + ax2.set_ylim(self.ylim) + ax2.set_aspect('equal', 'box') + ax2.set_xlabel('X [m]') + #ax2.set_ylabel('Y [m]') + ax2.set_title('TO traj.') + #ax.legend() + ax1.grid(True) + ax2.grid(True) + + plt.savefig('{}/N_try_{}/ee_traj_{}_{}'.format(self.conf.Fig_path,self.N_try,init,update_step_counter)) + + + + +if __name__ == '__main__': + import os + import sys + import time + import random + import importlib + import numpy as np + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # {'0' -> show all logs, '1' -> filter out info, '2' -> filter out warnings} + import tensorflow as tf + import matplotlib.pyplot as plt + import mpl_toolkits.mplot3d.art3d as art3d + + from RL import RL_AC + from plot_utils import PLOT + from NeuralNetwork import NN + + ### Input ### + N_try = 0 + + seed = 0 + tf.random.set_seed(seed) # Set tensorflow seed + random.seed(seed) # Set random seed + + system_id = 'car_park' + + TO_method = 'casadi' + + recover_training_flag = 0 + + CPU_flag = 0 + if CPU_flag: + os.environ["CUDA_VISIBLE_DEVICES"]="-1" + tf.config.experimental.list_physical_devices('GPU') + + nb_cpus = 1 + + w_S = 0 + ################################# + + # Import configuration file and environment file + system_map = { + 'single_integrator': ('conf_single_integrator', 'SingleIntegrator'), + 'double_integrator': ('conf_double_integrator', 'DoubleIntegrator'), + 'car': ('conf_car', 'Car'), + 'car_park': ('conf_car_park', 'CarPark'), + 'manipulator': ('conf_manipulator', 'Manipulator'), + 'ur5': ('conf_ur5', 'UR5') + } + + try: + conf_module, env_class = system_map[system_id] + conf = importlib.import_module(conf_module) + Environment = getattr(importlib.import_module('environment'), env_class) + except KeyError: + print('System {} not found'.format(system_id)) + sys.exit() + + + + # Create folders to store the results and the trained NNs and save configuration + for path in conf.path_list: + os.makedirs(path + '/N_try_{}'.format(N_try), exist_ok=True) + os.makedirs(conf.Config_path, exist_ok=True) + + params = [p for p in conf.__dict__ if not p.startswith("__")] + with open(conf.Config_path + '/config{}.txt'.format(N_try), 'w') as f: + for p in params: + f.write('{} = {}\n'.format(p, conf.__dict__[p])) + f.write('Seed = {}\n'.format(seed)) + f.write('w_S = {}'.format(w_S)) + + + + # Create environment instances + env = Environment(conf) + + # Create NN instance + NN_inst = NN(env, conf, w_S) + + # Create RL_AC instance + RLAC = RL_AC(env, NN_inst, conf, N_try) + + # Set initial weights of the NNs, initialize the counter of the updates and setup NN models + if recover_training_flag: + recover_training = np.array([conf.NNs_path_rec, conf.N_try_rec, conf.update_step_counter_rec]) + update_step_counter = conf.update_step_counter_rec + nb_starting_episode = (conf.update_step_counter_rec/conf.UPDATE_LOOPS)+1 + + RLAC.setup_model(recover_training) + else: + update_step_counter = 0 + nb_starting_episode = 0 + + RLAC.setup_model() + + # Create PLOT instance + plot_fun = PLOT(N_try, env, NN_inst, conf) + + plot_fun.plot_Reward() \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_1/replay_buffer.py b/Results Single Integrator/Results set test/Code/N_try_1/replay_buffer.py new file mode 100644 index 0000000..ba1804d --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/replay_buffer.py @@ -0,0 +1,240 @@ +import math +import random +import numpy as np +import tensorflow as tf +import segment_tree +#from stable_baselines.common.segment_tree import SumSegmentTree, MinSegmentTree + + +class ReplayBuffer(object): + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param BATCH_SIZE : (int) Size of the mini-batch + :param nb_state : (int) State size (robot state size + 1) + ''' + + self.conf = conf + self.storage_mat = np.zeros((conf.REPLAY_SIZE, conf.nb_state + 1 + conf.nb_state + conf.nb_state + 1 + 1)) + self.next_idx = 0 + self.full = 0 + self.exp_counter = np.zeros(conf.REPLAY_SIZE) + + def add(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Add transitions to the buffer ''' + data = self.concatenate_sample(obses_t, rewards, obses_t1, dVdxs, dones, terms) + + if len(data) + self.next_idx > self.conf.REPLAY_SIZE: + self.storage_mat[self.next_idx:,:] = data[:self.conf.REPLAY_SIZE-self.next_idx,:] + self.storage_mat[:self.next_idx+len(data)-self.conf.REPLAY_SIZE,:] = data[self.conf.REPLAY_SIZE-self.next_idx:,:] + self.full = 1 + else: + self.storage_mat[self.next_idx:self.next_idx+len(data),:] = data + + self.next_idx = (self.next_idx + len(data)) % self.conf.REPLAY_SIZE + + def sample(self): + ''' Sample a batch of transitions ''' + # Select indexes of the batch elements + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + idxes = np.random.randint(0, max_idx, size=self.conf.BATCH_SIZE) + + obses_t = self.storage_mat[idxes, :self.conf.nb_state] + rewards = self.storage_mat[idxes, self.conf.nb_state:self.conf.nb_state+1] + obses_t1 = self.storage_mat[idxes, self.conf.nb_state+1:self.conf.nb_state*2+1] + dVdxs = self.storage_mat[idxes, self.conf.nb_state*2+1:self.conf.nb_state*3+1] + dones = self.storage_mat[idxes, self.conf.nb_state*3+1:self.conf.nb_state*3+2] + terms = self.storage_mat[idxes, self.conf.nb_state*3+2:self.conf.nb_state*3+3] + + # Priorities not used + weights = np.ones((self.conf.BATCH_SIZE,1)) + batch_idxes = None + + # Convert the sample in tensor + obses_t, rewards, obses_t1, dVdxs, dones, weights = self.convert_sample_to_tensor(obses_t, rewards, obses_t1, dVdxs, dones, weights) + + return obses_t, rewards, obses_t1, dVdxs, dones, terms, weights, batch_idxes + + def concatenate_sample(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Convert batch of transitions into a tensor ''' + obses_t = np.concatenate(obses_t, axis=0) + rewards = np.concatenate(rewards, axis=0) + obses_t1 = np.concatenate(obses_t1, axis=0) + dVdxs = np.concatenate(dVdxs, axis=0) + dones = np.concatenate(dones, axis=0) + terms = np.concatenate(terms, axis=0) + + return np.concatenate((obses_t, rewards.reshape(-1,1), obses_t1, dVdxs, dones.reshape(-1,1), terms.reshape(-1,1)),axis=1) + + def convert_sample_to_tensor(self, obses_t, rewards, obses_t1, dVdxs, dones, weights): + ''' Convert batch of transitions into a tensor ''' + obses_t = tf.convert_to_tensor(obses_t, dtype=tf.float32) + rewards = tf.convert_to_tensor(rewards, dtype=tf.float32) + obses_t1 = tf.convert_to_tensor(obses_t1, dtype=tf.float32) + dVdxs = tf.convert_to_tensor(dVdxs, dtype=tf.float32) + dones = tf.convert_to_tensor(dones, dtype=tf.float32) + weights = tf.convert_to_tensor(weights, dtype=tf.float32) + + return obses_t, rewards, obses_t1, dVdxs, dones, weights + + + +class PrioritizedReplayBuffer: + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param BATCH_SIZE : (int) Size of the mini-batch + :param nb_state : (int) State size (robot state size + 1) + :param prioritized_replay_alpha : (float) Determines how much prioritization is used, set to 0 to use a normal buffer + :param prioritized_replay_beta : (float) Small positive constant that prevents the edge-case of transitions not being revisited once their error is zero + ''' + + self.conf = conf + + self.storage_mat = np.zeros((conf.REPLAY_SIZE, conf.nb_state + 1 + conf.nb_state + conf.nb_state + 1 + 1)) + self.full = 0 + self.next_idx = 0 + self.exp_counter = np.zeros(self.conf.REPLAY_SIZE) + self.priorities = np.empty(self.conf.REPLAY_SIZE) + + assert conf.prioritized_replay_alpha >= 0 + assert conf.prioritized_replay_beta > 0 + + it_capacity = 1 + while it_capacity < self.conf.REPLAY_SIZE: + it_capacity *= 2 + + self._it_sum = SumSegmentTree(it_capacity) + self._it_min = MinSegmentTree(it_capacity) + self._max_priority = 1.0 + + #self.RB_type = 'ReLO' + + self.MSE = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE) + + def add(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Add transitions to the buffer ''' + data = self.concatenate_sample(obses_t, rewards, obses_t1, dVdxs, dones, terms) + + if len(data) + self.next_idx > self.conf.REPLAY_SIZE: + self.storage_mat[self.next_idx:,:] = data[:self.conf.REPLAY_SIZE-self.next_idx,:] + self.storage_mat[:self.next_idx+len(data)-self.conf.REPLAY_SIZE,:] = data[self.conf.REPLAY_SIZE-self.next_idx:,:] + self.full = 1 + else: + self.storage_mat[self.next_idx:self.next_idx+len(data),:] = data + + for i in range(len(data)): + self._it_sum[(self.next_idx+i) % self.conf.REPLAY_SIZE] = self._max_priority ** self.conf.prioritized_replay_alpha + self._it_min[(self.next_idx+i) % self.conf.REPLAY_SIZE] = self._max_priority ** self.conf.prioritized_replay_alpha + + self.next_idx = (self.next_idx + len(data)) % self.conf.REPLAY_SIZE + + def _sample_proportional(self): + ''' Sample a batch of transitions ''' + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + + idx_arr = np.zeros(self.conf.BATCH_SIZE) + + p_total = self._it_sum.sum(0, max_idx - 1) + + segment = p_total / self.conf.BATCH_SIZE + + for i in range(self.conf.BATCH_SIZE): + p = random.random() * segment + i * segment #random.random(segment*i, segment*(i+1)) + idx = self._it_sum.find_prefixsum_idx(p) + idx_arr[i] = idx + + return idx_arr + + def sample(self): + ''' Sample a batch of transitions ''' + # Select indexes of the batch elements + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + + idxes = self._sample_proportional() + batch_idxes = idxes.astype(int) + + # Compute weights normalization + p_min = self._it_min.min() / self._it_sum.sum() + max_weight = (p_min * max_idx) ** (-self.conf.prioritized_replay_beta) + + self.exp_counter[batch_idxes] += 1 + self.priorities[batch_idxes] = self._it_sum[batch_idxes] / self._it_sum.sum() + weights = (self.priorities[batch_idxes] * max_idx) ** (-self.conf.prioritized_replay_beta) / max_weight + + obses_t = self.storage_mat[batch_idxes, :self.conf.nb_state] + rewards = self.storage_mat[batch_idxes, self.conf.nb_state:self.conf.nb_state+1] + obses_t1 = self.storage_mat[batch_idxes, self.conf.nb_state+1:self.conf.nb_state*2+1] + dVdxs = self.storage_mat[batch_idxes, self.conf.nb_state*2+1:self.conf.nb_state*3+1] + dones = self.storage_mat[batch_idxes, self.conf.nb_state*3+1:self.conf.nb_state*3+2] + terms = self.storage_mat[batch_idxes, self.conf.nb_state*3+2:self.conf.nb_state*3+3] + + # Convert the sample in tensor + obses_t, rewards, obses_t1, dVdxs, dones, weights = self.convert_sample_to_tensor(obses_t, rewards, obses_t1, dVdxs, dones, weights) + + return obses_t, rewards, obses_t1, dVdxs, dones, terms, weights, batch_idxes + + def update_priorities(self, idxes, reward_to_go_batch, critic_value, target_critic_value=None): + '''Update priorities of sampled transitions ''' + # Create td_errors + if self.RB_type == 'ReLO': + # MSE(Delta V_c) - MSE(Delta V_t) + td_errors = self.MSE(reward_to_go_batch, critic_value).numpy()-self.MSE(reward_to_go_batch, target_critic_value).numpy() + td_errors_norm = np.clip(td_errors, 0, np.max(td_errors)) + else: # 'PER' is default self.RB_type + # |TD_error_i| + td_errors_norm = tf.math.abs(tf.math.subtract(reward_to_go_batch, critic_value)) + td_errors_norm = td_errors_norm[:,0] + + # Compute the freshness discount factor + fresh_disc_factor = self.conf.fresh_factor**self.exp_counter[idxes] + + # Compute new priorities: p_i = mu**C_i * td_error + self.conf.prioritized_replay_eps + new_priorities = fresh_disc_factor * td_errors_norm + self.conf.prioritized_replay_eps + + # Sets priority of transition at index idxes[i] in buffer to priorities[i] + assert len(idxes) == len(new_priorities) + for idx, priority in zip(idxes, new_priorities): + assert priority > 0 + + idx = int(idx) + + self._it_sum[idx] = priority ** self.conf.prioritized_replay_alpha + self._it_min[idx] = priority ** self.conf.prioritized_replay_alpha + + self._max_priority = max(self._max_priority, priority) + + def concatenate_sample(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Convert batch of transitions into a tensor ''' + obses_t = np.concatenate(obses_t, axis=0) + rewards = np.concatenate(rewards, axis=0) + obses_t1 = np.concatenate(obses_t1, axis=0) + dVdxs = np.concatenate(dVdxs, axis=0) + dones = np.concatenate(dones, axis=0) + terms = np.concatenate(terms, axis=0) + + return np.concatenate((obses_t, rewards.reshape(-1,1), obses_t1, dVdxs, dones.reshape(-1,1), terms.reshape(-1,1)),axis=1) + + def convert_sample_to_tensor(self, obses_t, rewards, obses_t1, dVdxs, dones, weights): + ''' Convert batch of transitions into a tensor ''' + obses_t = tf.convert_to_tensor(obses_t, dtype=tf.float32) + rewards = tf.convert_to_tensor(rewards, dtype=tf.float32) + obses_t1 = tf.convert_to_tensor(obses_t1, dtype=tf.float32) + dVdxs = tf.convert_to_tensor(dVdxs, dtype=tf.float32) + dones = tf.convert_to_tensor(dones, dtype=tf.float32) + weights = tf.convert_to_tensor(weights, dtype=tf.float32) + + return obses_t, rewards, obses_t1, dVdxs, dones, weights diff --git a/Results Single Integrator/Results set test/Code/N_try_1/robot_utils.py b/Results Single Integrator/Results set test/Code/N_try_1/robot_utils.py new file mode 100644 index 0000000..815ff31 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/robot_utils.py @@ -0,0 +1,637 @@ +import os +import math +import random +import gepetto#.corbaserver +import time +import subprocess +import numpy as np +import pinocchio as pin +from numpy.linalg import norm +from pinocchio.robot_wrapper import RobotWrapper as PinocchioRobotWrapper + +class Contact: + ''' A contact between a contact-point and a contact-surface + ''' + def __init__(self, contact_point, contact_surface): + self.cp = contact_point + self.cs = contact_surface + self.reset_contact_position() + + def reset_contact_position(self): + # Initialize anchor point p0, that is the initial (0-load) position of the spring + self.p0 = self.cp.get_position() + self.in_contact = True + + def compute_force(self): + self.f, self.p0 = self.cs.compute_force(self.cp, self.p0) + return self.f + + def get_jacobian(self): + return self.cp.get_jacobian() + +class RobotWrapper(PinocchioRobotWrapper): + + @staticmethod + def BuildFromURDF(filename, package_dirs=None, root_joint=None, verbose=False, meshLoader=None): + robot = RobotWrapper() + robot.initFromURDF(filename, package_dirs, root_joint, verbose, meshLoader) + return robot + + @property + def na(self): + if(self.model.joints[0].nq==7): + return self.model.nv-6 + return self.model.nv + + def mass(self, q, update=True): + if(update): + return pin.crba(self.model, self.data, q) + return self.data.M + + def nle(self, q, v, update=True): + if(update): + return pin.nonLinearEffects(self.model, self.data, q, v) + return self.data.nle + + def com(self, q=None, v=None, a=None, update=True): + if(update==False or q is None): + return PinocchioRobotWrapper.com(self, q) + if a is None: + if v is None: + return PinocchioRobotWrapper.com(self, q) + return PinocchioRobotWrapper.com(self, q, v) + return PinocchioRobotWrapper.com(self, q, v,a) + + def Jcom(self, q, update=True): + if(update): + return pin.jacobianCenterOfMass(self.model, self.data, q) + return self.data.Jcom + + def momentumJacobian(self, q, v, update=True): + if(update): + pin.ccrba(self.model, self.data, q, v) + return self.data.Ag + + def computeAllTerms(self, q, v): + ''' pin.computeAllTerms is equivalent to calling: + pinocchio::forwardKinematics + pinocchio::crba + pinocchio::nonLinearEffects + pinocchio::computeJointJacobians + pinocchio::centerOfMass + pinocchio::jacobianCenterOfMass + pinocchio::kineticEnergy + pinocchio::potentialEnergy + This is too much for our needs, so we call only the functions + we need, including those for the frame kinematics + ''' + #pin.computeAllTerms(self.model, self.data, q, v); + pin.forwardKinematics(self.model, self.data, q, v, np.zeros(self.model.nv)) + pin.computeJointJacobians(self.model, self.data) + pin.updateFramePlacements(self.model, self.data) + pin.crba(self.model, self.data, q) + pin.nonLinearEffects(self.model, self.data, q, v) + + def forwardKinematics(self, q, v=None, a=None): + if v is not None: + if a is not None: + pin.forwardKinematics(self.model, self.data, q, v, a) + else: + pin.forwardKinematics(self.model, self.data, q, v) + else: + pin.forwardKinematics(self.model, self.data, q) + + def inverseKinematics(self, ee_coords): + ''' Inverse kinematics algorithm to compute a joint configuration given the EE coordinates''' + + oMdes = pin.SE3(np.eye(3), np.array([ee_coords[0], ee_coords[1], 0.0])) + random.seed(123) + for j in range(100): + q = np.array([random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi)]) + # q = pin.neutral(self.model) + + pin.forwardKinematics(self.model, self.data, q) + pin.updateFramePlacements(self.model, self.data) + eps = 1e-4 + IT_MAX = 1000 + DT = 1e-1 + damp = 1e-12 + frame_id = self.model.getFrameId('fixed_ee') + i=0 + while True: + pin.forwardKinematics(self.model,self.data,q) + pin.updateFramePlacements(self.model, self.data) + dMi = oMdes.actInv(self.data.oMf[frame_id]) + err = pin.log(dMi).vector + if norm(err) < eps: + success = True + break + if i >= IT_MAX: + success = False + break + J = pin.computeFrameJacobian(self.model,self.data,q,frame_id) + v = - J.T.dot(np.linalg.solve(J.dot(J.T) + damp * np.eye(6), err)) + q = pin.integrate(self.model,q,v*DT) + if not i % 10: + print('%d: error = %s' % (i, err.T)) + i += 1 + + if success: + print("Convergence achieved!") + break + else: + print("\nWarning: the iterative algorithm has not reached convergence to the desired precision. Retry ({}) with another initial configuration".format(j)) + + return q, success + + def frameJacobian(self, q, index, update=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + ''' Call computeFrameJacobian if update is true. If not, user should call computeFrameJacobian first. + Then call getFrameJacobian and return the Jacobian matrix. + ref_frame can be: ReferenceFrame.LOCAL, ReferenceFrame.WORLD, ReferenceFrame.LOCAL_WORLD_ALIGNED + ''' + if(update): + pin.computeFrameJacobian(self.model, self.data, q, index) + return pin.getFrameJacobian(self.model, self.data, index, ref_frame) + + def frameVelocity(self, q, v, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v) + v_local = pin.getFrameVelocity(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return v_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + v_world = H.act(v_local) + return v_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + v = Hr.act(v_local) + return v + + def frameAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def frameClassicAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + v = pin.getFrameVelocity(self.model, self.data, index) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + a_local.linear += np.cross(v.angular, v.linear, axis=0) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def deactivateCollisionPairs(self, collision_pair_indexes): + for i in collision_pair_indexes: + self.collision_data.deactivateCollisionPair(i) + + def addAllCollisionPairs(self): + self.collision_model.addAllCollisionPairs() + self.collision_data = pin.GeometryData(self.collision_model) + + def isInCollision(self, q, stop_at_first_collision=True): + return pin.computeCollisions(self.model, self.data, self.collision_model, self.collision_data, np.asmatrix(q).reshape((self.model.nq,1)), stop_at_first_collision) + + def findFirstCollisionPair(self, consider_only_active_collision_pairs=True): + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + return (i, self.collision_model.collisionPairs[i]) + return None + + def findAllCollisionPairs(self, consider_only_active_collision_pairs=True): + res = [] + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + res += [(i, self.collision_model.collisionPairs[i])] + return res + +class RobotSimulator: + + def __init__(self, robot, q0_init, v0_init, simulation_type, tau_coulomb_max, use_viewer=False, DISPLAY_T=None, CAMERA_TRANSFORM=None, show_floor=False): + self.robot = robot + self.model = self.robot.model + self.data = self.model.createData() + self.t = 0.0 # time + self.nv = nv = self.model.nv # Dimension of joint velocities vector + self.na = na = robot.na # number of actuated joints + self.S = np.hstack((np.zeros((na, nv-na)), np.eye(na, na))) # Matrix S used as filter of vector of inputs U + + self.contacts = [] + self.candidate_contact_points = [] # candidate contact points + self.contact_surfaces = [] + + self.DISPLAY_T = DISPLAY_T # refresh period for viewer + self.use_viewer = use_viewer + self.tau_coulomb_max = tau_coulomb_max + self.display_counter = self.DISPLAY_T + self.init(q0_init, v0_init, True) + + self.tau_c = np.zeros(na) # Coulomb friction torque + self.simulation_type = simulation_type + self.set_coulomb_friction(tau_coulomb_max) + + # for gepetto viewer + self.gui = None + if(use_viewer): + try: + prompt = subprocess.getstatusoutput("ps aux |grep 'gepetto-gui'|grep -v 'grep'|wc -l") + if int(prompt[1]) == 0: + os.system('gepetto-gui &') + time.sleep(1) + except: + pass + gepetto.corbaserver.Client() + gepetto.Client() + self.robot.initViewer(loadModel=True) + self.gui = self.robot.viewer.gui + if(show_floor): + self.robot.viewer.gui.createSceneWithFloor('world') + self.gui.setLightingMode('world/floor', 'OFF') + self.robot.displayCollisions(False) + self.robot.displayVisuals(True) + self.robot.display(self.q) + try: + self.gui.setCameraTransform("python-pinocchio", CAMERA_TRANSFORM) + except: + self.gui.setCameraTransform(0, CAMERA_TRANSFORM) + + # Re-initialize the simulator + def init(self, q0=None, v0=None, reset_contact_positions=False): + self.first_iter = True + + if q0 is not None: + self.q = q0.copy() + + if(v0 is None): + self.v = np.zeros(self.robot.nv) + else: + self.v = v0.copy() + self.dv = np.zeros(self.robot.nv) + self.resize_contact_data(reset_contact_positions) + + def resize_contact_data(self, reset_contact_positions=False): + self.nc = len(self.contacts) # number of contacts + self.nk = 3*self.nc # size of contact force vector + self.f = np.zeros(self.nk) # contact forces + self.Jc = np.zeros((self.nk, self.model.nv)) # contact Jacobian + + # reset contact position + if(reset_contact_positions): + pin.forwardKinematics(self.model, self.data, self.q) + pin.updateFramePlacements(self.model, self.data) + for c in self.contacts: + c.reset_contact_position() + + self.compute_forces(compute_data=True) + + def set_coulomb_friction(self, tau_max): + self.tau_coulomb_max = 1e-2*tau_max*self.model.effortLimit + self.simulate_coulomb_friction = (norm(self.tau_coulomb_max)!=0.0) + + def collision_detection(self): + for s in self.contact_surfaces: # for each contact surface + for cp in self.candidate_contact_points: # for each candidate contact point + p = cp.get_position() + if(s.check_collision(p)): # check whether the point is colliding with the surface + if(not cp.active): # if the contact was not already active + print("Collision detected between point", cp.frame_name, " at ", p) + cp.active = True + cp.contact = Contact(cp, s) + self.contacts += [cp.contact] + self.resize_contact_data() + else: + if(cp.active): + print("Contact lost between point", cp.frame_name, " at ", p) + cp.active = False + self.contacts.remove(cp.contact) + self.resize_contact_data() + + def compute_forces(self, compute_data=True): + '''Compute the contact forces from q, v and elastic model''' + if compute_data: + pin.forwardKinematics(self.model, self.data, self.q, self.v) + # Computes the placements of all the operational frames according to the current joint placement stored in data + pin.updateFramePlacements(self.model, self.data) + self.collision_detection() + + i = 0 + for c in self.contacts: + self.f[i:i+3] = c.compute_force() + self.Jc[i:i+3, :] = c.get_jacobian() + i += 3 + return self.f + + def step(self, u, dt=None): + if dt is None: + dt = self.dt + + # (Forces are directly in the world frame, and aba wants them in the end effector frame) + pin.computeAllTerms(self.model, self.data, self.q, self.v) + pin.updateFramePlacements(self.model, self.data) + M = self.data.M + h = self.data.nle + self.collision_detection() + self.compute_forces(False) + + if(self.simulate_coulomb_friction and self.simulation_type=='timestepping'): + # minimize kinetic energy using time stepping + from quadprog import solve_qp + ''' + Solve a strictly convex quadratic program + + Minimize 1/2 x^T G x - a^T x + Subject to C.T x >= b + + Input Parameters + ---------- + G : array, shape=(n, n) + a : array, shape=(n,) + C : array, shape=(n, m) matrix defining the constraints + b : array, shape=(m), default=None, vector defining the constraints + meq : int, default=0 + the first meq constraints are treated as equality constraints, + all further as inequality constraints + ''' + # M (v' - v) = dt*S^T*(tau - tau_c) - dt*h + dt*J^T*f + # M v' = M*v + dt*(S^T*tau - h + J^T*f) - dt*S^T*tau_c + # M v' = b + B*tau_c + # v' = Minv*(b + B*tau_c) + b = M.dot(self.v) + dt*(self.S.T.dot(u) - h + self.Jc.T.dot(self.f)) + B = - dt*self.S.T + # Minimize kinetic energy: + # min v'.T * M * v' + # min (b+B*tau_c​).T*Minv*(b+B*tau_c​) + # min tau_c.T * B.T*Minv*B* tau_C + 2*b.T*Minv*B*tau_c + Minv = np.linalg.inv(M) + G = B.T.dot(Minv.dot(B)) + a = -b.T.dot(Minv.dot(B)) + C = np.vstack((np.eye(self.na), -np.eye(self.na))) + c = np.concatenate((-self.tau_coulomb_max, -self.tau_coulomb_max)) + solution = solve_qp(G, a, C.T, c, 0) + self.tau_c = solution[0] + self.v = Minv.dot(b + B.dot(self.tau_c)) + self.q = pin.integrate(self.model, self.q, self.v*dt) + self.dv = np.linalg.solve(M, self.S.T.dot(u-self.tau_c) - h + self.Jc.T.dot(self.f)) + elif(self.simulation_type=='euler' or self.simulate_coulomb_friction==False): + self.tau_c = self.tau_coulomb_max*np.sign(self.v[-self.na:]) + self.dv = np.linalg.solve(M, self.S.T.dot(u-self.tau_c) - h + self.Jc.T.dot(self.f)) + # v_mean = np.copy(self.v) + 0.5*dt*self.dv + self.q = pin.integrate(self.model, self.q, self.v*dt) + # self.q += dt*v_mean + self.v += self.dv*dt + else: + print("[ERROR] Unknown simulation type:", self.simulation_type) + + self.t += dt + return self.q, self.v, self.dv + + def reset(self): + self.first_iter = True + + def simulate(self, state, u, dt=0.05, ndt=1): + ''' Perform ndt steps, each lasting dt/ndt seconds ''' + self.q = state[:self.robot.nq] + self.v = state[self.robot.nq:self.robot.nv+self.robot.nq] + + tau_c_avg = 0*self.tau_c + for i in range(ndt): + self.q, self.v, self.dv = self.step(u, dt/ndt) + tau_c_avg += self.tau_c + self.tau_c = tau_c_avg / ndt + + if (self.use_viewer): + self.display_counter -= dt + if self.display_counter <= 0.0: + self.robot.display(self.q) + self.display_counter = self.DISPLAY_T + + return self.q, self.v, self.f + + def display(self, q): + if(self.use_viewer): + self.robot.display(q) + + +class RobotWrapper(PinocchioRobotWrapper): + + @staticmethod + def BuildFromURDF(filename, package_dirs=None, root_joint=None, verbose=False, meshLoader=None): + robot = RobotWrapper() + robot.initFromURDF(filename, package_dirs, root_joint, verbose, meshLoader) + return robot + + @property + def na(self): + if(self.model.joints[0].nq==7): + return self.model.nv-6 + return self.model.nv + + def mass(self, q, update=True): + if(update): + return pin.crba(self.model, self.data, q) + return self.data.M + + def nle(self, q, v, update=True): + if(update): + return pin.nonLinearEffects(self.model, self.data, q, v) + return self.data.nle + + def com(self, q=None, v=None, a=None, update=True): + if(update==False or q is None): + return PinocchioRobotWrapper.com(self, q) + if a is None: + if v is None: + return PinocchioRobotWrapper.com(self, q) + return PinocchioRobotWrapper.com(self, q, v) + return PinocchioRobotWrapper.com(self, q, v,a) + + def Jcom(self, q, update=True): + if(update): + return pin.jacobianCenterOfMass(self.model, self.data, q) + return self.data.Jcom + + def momentumJacobian(self, q, v, update=True): + if(update): + pin.ccrba(self.model, self.data, q, v) + return self.data.Ag + + def computeAllTerms(self, q, v): + ''' pin.computeAllTerms is equivalent to calling: + pinocchio::forwardKinematics + pinocchio::crba + pinocchio::nonLinearEffects + pinocchio::computeJointJacobians + pinocchio::centerOfMass + pinocchio::jacobianCenterOfMass + pinocchio::kineticEnergy + pinocchio::potentialEnergy + This is too much for our needs, so we call only the functions + we need, including those for the frame kinematics + ''' + #pin.computeAllTerms(self.model, self.data, q, v); + pin.forwardKinematics(self.model, self.data, q, v, np.zeros(self.model.nv)) + pin.computeJointJacobians(self.model, self.data) + pin.updateFramePlacements(self.model, self.data) + pin.crba(self.model, self.data, q) + pin.nonLinearEffects(self.model, self.data, q, v) + + def forwardKinematics(self, q, v=None, a=None): + if v is not None: + if a is not None: + pin.forwardKinematics(self.model, self.data, q, v, a) + else: + pin.forwardKinematics(self.model, self.data, q, v) + else: + pin.forwardKinematics(self.model, self.data, q) + + def inverseKinematics(self, ee_coords): + ''' Inverse kinematics algorithm to compute a joint configuration given the EE coordinates''' + + oMdes = pin.SE3(np.eye(3), np.array([ee_coords[0], ee_coords[1], 0.0])) + random.seed(123) + for j in range(100): + q = np.array([random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi)]) + # q = pin.neutral(self.model) + + pin.forwardKinematics(self.model, self.data, q) + pin.updateFramePlacements(self.model, self.data) + eps = 1e-4 + IT_MAX = 1000 + DT = 1e-1 + damp = 1e-12 + frame_id = self.model.getFrameId('fixed_ee') + i=0 + while True: + pin.forwardKinematics(self.model,self.data,q) + pin.updateFramePlacements(self.model, self.data) + dMi = oMdes.actInv(self.data.oMf[frame_id]) + err = pin.log(dMi).vector + if norm(err) < eps: + success = True + break + if i >= IT_MAX: + success = False + break + J = pin.computeFrameJacobian(self.model,self.data,q,frame_id) + v = - J.T.dot(np.linalg.solve(J.dot(J.T) + damp * np.eye(6), err)) + q = pin.integrate(self.model,q,v*DT) + if not i % 10: + print('%d: error = %s' % (i, err.T)) + i += 1 + + if success: + print("Convergence achieved!") + break + else: + print("\nWarning: the iterative algorithm has not reached convergence to the desired precision. Retry ({}) with another initial configuration".format(j)) + + return q, success + + def frameJacobian(self, q, index, update=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + ''' Call computeFrameJacobian if update is true. If not, user should call computeFrameJacobian first. + Then call getFrameJacobian and return the Jacobian matrix. + ref_frame can be: ReferenceFrame.LOCAL, ReferenceFrame.WORLD, ReferenceFrame.LOCAL_WORLD_ALIGNED + ''' + if(update): + pin.computeFrameJacobian(self.model, self.data, q, index) + return pin.getFrameJacobian(self.model, self.data, index, ref_frame) + + def frameVelocity(self, q, v, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v) + v_local = pin.getFrameVelocity(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return v_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + v_world = H.act(v_local) + return v_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + v = Hr.act(v_local) + return v + + def frameAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def frameClassicAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + v = pin.getFrameVelocity(self.model, self.data, index) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + a_local.linear += np.cross(v.angular, v.linear, axis=0) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def deactivateCollisionPairs(self, collision_pair_indexes): + for i in collision_pair_indexes: + self.collision_data.deactivateCollisionPair(i) + + def addAllCollisionPairs(self): + self.collision_model.addAllCollisionPairs() + self.collision_data = pin.GeometryData(self.collision_model) + + def isInCollision(self, q, stop_at_first_collision=True): + return pin.computeCollisions(self.model, self.data, self.collision_model, self.collision_data, np.asmatrix(q).reshape((self.model.nq,1)), stop_at_first_collision) + + def findFirstCollisionPair(self, consider_only_active_collision_pairs=True): + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + return (i, self.collision_model.collisionPairs[i]) + return None + + def findAllCollisionPairs(self, consider_only_active_collision_pairs=True): + res = [] + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + res += [(i, self.collision_model.collisionPairs[i])] + return res \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_1/segment_tree.py b/Results Single Integrator/Results set test/Code/N_try_1/segment_tree.py new file mode 100644 index 0000000..cb386ec --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/segment_tree.py @@ -0,0 +1,145 @@ +import operator + + +class SegmentTree(object): + def __init__(self, capacity, operation, neutral_element): + """Build a Segment Tree data structure. + + https://en.wikipedia.org/wiki/Segment_tree + + Can be used as regular array, but with two + important differences: + + a) setting item's value is slightly slower. + It is O(lg capacity) instead of O(1). + b) user has access to an efficient ( O(log segment size) ) + `reduce` operation which reduces `operation` over + a contiguous subsequence of items in the array. + + Paramters + --------- + capacity: int + Total size of the array - must be a power of two. + operation: lambda obj, obj -> obj + and operation for combining elements (eg. sum, max) + must form a mathematical group together with the set of + possible values for array elements (i.e. be associative) + neutral_element: obj + neutral element for the operation above. eg. float('-inf') + for max and 0 for sum. + """ + assert capacity > 0 and capacity & (capacity - 1) == 0, "capacity must be positive and a power of 2." + self._capacity = capacity + self._value = [neutral_element for _ in range(2 * capacity)] + self._operation = operation + + def _reduce_helper(self, start, end, node, node_start, node_end): + if start == node_start and end == node_end: + return self._value[node] + mid = (node_start + node_end) // 2 + if end <= mid: + return self._reduce_helper(start, end, 2 * node, node_start, mid) + else: + if mid + 1 <= start: + return self._reduce_helper(start, end, 2 * node + 1, mid + 1, node_end) + else: + return self._operation( + self._reduce_helper(start, mid, 2 * node, node_start, mid), + self._reduce_helper(mid + 1, end, 2 * node + 1, mid + 1, node_end) + ) + + def reduce(self, start=0, end=None): + """Returns result of applying `self.operation` + to a contiguous subsequence of the array. + + self.operation(arr[start], operation(arr[start+1], operation(... arr[end]))) + + Parameters + ---------- + start: int + beginning of the subsequence + end: int + end of the subsequences + + Returns + ------- + reduced: obj + result of reducing self.operation over the specified range of array elements. + """ + if end is None: + end = self._capacity + if end < 0: + end += self._capacity + end -= 1 + return self._reduce_helper(start, end, 1, 0, self._capacity - 1) + + def __setitem__(self, idx, val): + # index of the leaf + idx += self._capacity + self._value[idx] = val + idx //= 2 + while idx >= 1: + self._value[idx] = self._operation( + self._value[2 * idx], + self._value[2 * idx + 1] + ) + idx //= 2 + + def __getitem__(self, idx): + assert 0 <= idx < self._capacity + return self._value[self._capacity + idx] + + +class SumSegmentTree(SegmentTree): + def __init__(self, capacity): + super(SumSegmentTree, self).__init__( + capacity=capacity, + operation=operator.add, + neutral_element=0.0 + ) + + def sum(self, start=0, end=None): + """Returns arr[start] + ... + arr[end]""" + return super(SumSegmentTree, self).reduce(start, end) + + def find_prefixsum_idx(self, prefixsum): + """Find the highest index `i` in the array such that + sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum + + if array values are probabilities, this function + allows to sample indexes according to the discrete + probability efficiently. + + Parameters + ---------- + perfixsum: float + upperbound on the sum of array prefix + + Returns + ------- + idx: int + highest index satisfying the prefixsum constraint + """ + assert 0 <= prefixsum <= self.sum() + 1e-5 + idx = 1 + while idx < self._capacity: # while non-leaf + if self._value[2 * idx] > prefixsum: + idx = 2 * idx + else: + prefixsum -= self._value[2 * idx] + idx = 2 * idx + 1 + return idx - self._capacity + + +class MinSegmentTree(SegmentTree): + def __init__(self, capacity): + super(MinSegmentTree, self).__init__( + capacity=capacity, + operation=min, + neutral_element=float('inf') + ) + + def min(self, start=0, end=None): + """Returns min(arr[start], ..., arr[end])""" + + return super(MinSegmentTree, self).reduce(start, end) diff --git a/Results Single Integrator/Results set test/Code/N_try_1/utils.py b/Results Single Integrator/Results set test/Code/N_try_1/utils.py new file mode 100644 index 0000000..c426b89 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_1/utils.py @@ -0,0 +1,42 @@ +import tensorflow as tf +import numpy as np + +def array2tensor(array): + + return tf.expand_dims(tf.convert_to_tensor(array), 0) + +def de_normalize_tensor(state, state_norm_arr): + ''' Retrieve state from normalized state - tensor ''' + state_time = tf.concat([tf.zeros([state.shape[0], state.shape[1]-1]), tf.reshape((state[:,-1]+1)*state_norm_arr[-1]/2,[state.shape[0],1])],1) + state_no_time = state * state_norm_arr + mask = tf.concat([tf.ones([state.shape[0], state.shape[1]-1]), tf.zeros([state.shape[0], 1])],1) + state_not_norm = state_no_time * mask + state_time * (1 - mask) + + return state_not_norm + +def normalize_tensor(state, state_norm_arr): + ''' Retrieve state from normalized state - tensor ''' + state_norm_time = tf.concat([tf.zeros([state.shape[0], state.shape[1]-1]), tf.reshape(((state[:,-1]) / state_norm_arr[-1])*2 - 1,[state.shape[0],1])],1) + state_norm_no_time = state / state_norm_arr + mask = tf.concat([tf.ones([state.shape[0], state.shape[1]-1]), tf.zeros([state.shape[0], 1])],1) + state_norm = state_norm_no_time * mask + state_norm_time * (1 - mask) + + return state_norm + +def de_normalize(state, state_norm_arr): + ''' Retrieve state from normalized state ''' + state_not_norm = np.empty_like(state) + state_not_norm[:-1] = state[:-1] * state_norm_arr[:-1] + state_not_norm[-1] = (state[-1] + 1) * state_norm_arr[-1]/2 + + return state_not_norm + +def normalize(state, state_norm_arr): + ''' Normalize state ''' + state_norm = np.empty_like(state) + state_norm = state / state_norm_arr + state_norm[-1] = state_norm[-1] * 2 -1 + + return state_norm + + diff --git a/Results Single Integrator/Results set test/Code/N_try_100/NeuralNetwork.py b/Results Single Integrator/Results set test/Code/N_try_100/NeuralNetwork.py new file mode 100644 index 0000000..90f4127 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/NeuralNetwork.py @@ -0,0 +1,233 @@ +import math +import random +import numpy as np +import tensorflow as tf +from tensorflow.keras import layers, regularizers +from tf_siren import SinusodialRepresentationDense + +from utils import normalize_tensor + +class NN: + + def __init__(self, env, conf, w_S=0): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :param NH1: (int) 1st hidden layer size + :param NH2: (int) 2nd hidden layer size + :param kreg_l1_A : (float) Weight of L1 regularization in actor's network - kernel + :param kreg_l2_A : (float) Weight of L2 regularization in actor's network - kernel + :param breg_l1_A : (float) Weight of L2 regularization in actor's network - bias + :param breg_l2_A : (float) Weight of L2 regularization in actor's network - bias + :param kreg_l1_C : (float) Weight of L1 regularization in critic's network - kernel + :param kreg_l2_C : (float) Weight of L2 regularization in critic's network - kernel + :param breg_l1_C : (float) Weight of L1 regularization in critic's network - bias + :param breg_l2_C : (float) Weight of L2 regularization in critic's network - bias + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param NORMALIZE_INPUTS : (bool) Flag to normalize inputs (state) + :param state_norm_array : (float array) Array used to normalize states + :param MC : (bool) Flag to use MC or TD(n) + :param cost_weights_terminal : (float array) Running cost weights vector + :param cost_weights_running : (float array) Terminal cost weights vector + :param BATCH_SIZE : (int) Size of the mini-batch + :param dt : (float) Timestep + + :input w_S : (float) Sobolev-training weight + ''' + + self.env = env + self.conf = conf + + self.w_S = w_S + + self.MSE = tf.keras.losses.MeanSquaredError() + + return + + def create_actor(self): + ''' Create actor NN ''' + inputs = layers.Input(shape=(self.conf.nb_state,)) + + lay1 = layers.Dense(self.conf.NH1,kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(inputs) + leakyrelu1 = layers.LeakyReLU()(lay1) + lay2 = layers.Dense(self.conf.NH2, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(leakyrelu1) + leakyrelu2 = layers.LeakyReLU()(lay2) + outputs = layers.Dense(self.conf.nb_action, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(leakyrelu2) + + model = tf.keras.Model(inputs, outputs) + + return model + + def create_critic_elu(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = layers.Dense(16, activation='elu')(state_input) + state_out2 = layers.Dense(32, activation='elu')(state_out1) + out_lay1 = layers.Dense(256, activation='elu')(state_out2) + out_lay2 = layers.Dense(256, activation='elu')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_sine_elu(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = SinusodialRepresentationDense(64, activation='sine')(state_input) + state_out2 = layers.Dense(64, activation='elu')(state_out1) + out_lay1 = SinusodialRepresentationDense(128, activation='sine')(state_out2) + out_lay2 = layers.Dense(128, activation='elu')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_sine(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = SinusodialRepresentationDense(64, activation='sine')(state_input) + state_out2 = SinusodialRepresentationDense(64, activation='sine')(state_out1) + out_lay1 = SinusodialRepresentationDense(128, activation='sine')(state_out2) + out_lay2 = SinusodialRepresentationDense(128, activation='sine')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_relu(self): + ''' Create critic NN - relu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = layers.Dense(16, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(state_input) + leakyrelu1 = layers.LeakyReLU()(state_out1) + + state_out2 = layers.Dense(32, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu1) + leakyrelu2 = layers.LeakyReLU()(state_out2) + out_lay1 = layers.Dense(self.conf.NH1, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu2) + leakyrelu3 = layers.LeakyReLU()(out_lay1) + out_lay2 = layers.Dense(self.conf.NH2, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu3) + leakyrelu4 = layers.LeakyReLU()(out_lay2) + + outputs = layers.Dense(1, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu4) + + model = tf.keras.Model([state_input], outputs) + + return model + + def eval(self, NN, input): + ''' Compute the output of a NN given an input ''' + if not tf.is_tensor(input): + input = tf.convert_to_tensor(input, dtype=tf.float32) + + if self.conf.NORMALIZE_INPUTS: + input = normalize_tensor(input, self.conf.state_norm_arr) + + return NN(input, training=True) + + def custom_logarithm(self,input): + # Calculate the logarithms based on the non-zero condition + positive_log = tf.math.log(tf.math.maximum(input, 1e-7) + 1) + negative_log = -tf.math.log(tf.math.maximum(-input, 1e-7) + 1) + + # Use the appropriate logarithm based on the condition + result = tf.where(input > 0, positive_log, negative_log) + + return result + + def compute_critic_grad(self, critic_model, target_critic, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, weights_batch): + ''' Compute the gradient of the critic NN ''' + with tf.GradientTape() as tape: + # Compute value function tail if TD(n) is used + if self.conf.MC: + reward_to_go_batch = partial_reward_to_go_batch + else: + target_values = self.eval(target_critic, state_next_rollout_batch) # Compute Value at next state after conf.nsteps_TD_N steps given by target critic + reward_to_go_batch = partial_reward_to_go_batch + (1-d_batch)*target_values # Compute batch of 1-step targets for the critic loss + + # Compute critic loss + if self.w_S != 0: + with tf.GradientTape() as tape2: + tape2.watch(state_batch) + critic_value = self.eval(critic_model, state_batch) + der_critic_value = tape2.gradient(critic_value, state_batch) + + critic_loss_v = self.MSE(reward_to_go_batch, critic_value, sample_weight=weights_batch) + critic_loss_der = self.MSE(self.custom_logarithm(dVdx_batch[:,:-1]), self.custom_logarithm(der_critic_value[:,:-1]), sample_weight=weights_batch) # dV/dt not computed and so not used in the update + + critic_loss = critic_loss_der + self.w_S*critic_loss_v + else: + critic_value = self.eval(critic_model, state_batch) + critic_loss = self.MSE(reward_to_go_batch, critic_value, sample_weight=weights_batch) + + # Compute the gradients of the critic loss w.r.t. critic's parameters + critic_grad = tape.gradient(critic_loss, critic_model.trainable_variables) + + return critic_grad, reward_to_go_batch, critic_value, self.eval(target_critic, state_batch) + + def compute_actor_grad(self, actor_model, critic_model, state_batch, term_batch, batch_size): + ''' Compute the gradient of the actor NN ''' + if batch_size == None: + batch_size = self.conf.BATCH_SIZE + + actions = self.eval(actor_model, state_batch) + + # Both take into account normalization, ds_next_da is the gradient of the dynamics w.r.t. policy actions (ds'_da) + state_next_tf, ds_next_da = self.env.simulate_batch(state_batch.numpy(), actions.numpy()) , self.env.derivative_batch(state_batch.numpy(), actions.numpy()) + + with tf.GradientTape() as tape: + tape.watch(state_next_tf) + critic_value_next = self.eval(critic_model,state_next_tf) + + # dV_ds' = gradient of V w.r.t. s', where s'=f(s,a) a=policy(s) + dV_ds_next = tape.gradient(critic_value_next, state_next_tf) + + cost_weights_terminal_reshaped = np.reshape(self.conf.cost_weights_terminal,[1,len(self.conf.cost_weights_terminal)]) + cost_weights_running_reshaped = np.reshape(self.conf.cost_weights_running,[1,len(self.conf.cost_weights_running)]) + with tf.GradientTape() as tape1: + tape1.watch(actions) + rewards_tf = self.env.reward_batch(term_batch.dot(cost_weights_terminal_reshaped) + (1-term_batch).dot(cost_weights_running_reshaped), state_batch.numpy(), actions) + + # dr_da = gradient of reward r(s,a) w.r.t. policy's action a + dr_da = tape1.gradient(rewards_tf, actions, unconnected_gradients=tf.UnconnectedGradients.ZERO) + + dr_da_reshaped = tf.reshape(dr_da, (batch_size, 1, self.conf.nb_action)) + + # dr_ds' + dV_ds' (note: dr_ds' = 0) + dQ_ds_next = tf.reshape(dV_ds_next, (batch_size, 1, self.conf.nb_state)) + + # (dr_ds' + dV_ds')*ds'_da + dQ_ds_next_da = tf.matmul(dQ_ds_next, ds_next_da) + + # (dr_ds' + dV_ds')*ds'_da + dr_da + dQ_da = dQ_ds_next_da + dr_da_reshaped + + # Now let's multiply -[(dr_ds' + dV_ds')*ds'_da + dr_da] by the actions a + # and then let's autodifferentiate w.r.t theta_A (actor NN's parameters) to finally get -dQ/dtheta_A + with tf.GradientTape() as tape: + tape.watch(actor_model.trainable_variables) + actions = self.eval(actor_model, state_batch) + + actions_reshaped = tf.reshape(actions,(batch_size,self.conf.nb_action,1)) + dQ_da_reshaped = tf.reshape(dQ_da,(batch_size,1,self.conf.nb_action)) + Q_neg = tf.matmul(-dQ_da_reshaped,actions_reshaped) + + # Also here we need a scalar so we compute the mean -Q across the batch + mean_Qneg = tf.math.reduce_mean(Q_neg) + + # Gradients of the actor loss w.r.t. actor's parameters + actor_grad = tape.gradient(mean_Qneg, actor_model.trainable_variables) + + return actor_grad \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_100/RL.py b/Results Single Integrator/Results set test/Code/N_try_100/RL.py new file mode 100644 index 0000000..fa60798 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/RL.py @@ -0,0 +1,233 @@ +import uuid +import math +import numpy as np +import tensorflow as tf + +class RL_AC: + def __init__(self, env, NN, conf, N_try): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :parma critic_type : (str) Activation function to use for the critic NN + :param LR_SCHEDULE : (bool) Flag to use a scheduler for the learning rates + :param boundaries_schedule_LR_C : (list) Boudaries of critic LR + :param values_schedule_LR_C : (list) Values of critic LR + :param boundaries_schedule_LR_A : (list) Boudaries of actor LR + :param values_schedule_LR_A : (list) Values of actor LR + :param CRITIC_LEARNING_RATE : (float) Learning rate for the critic network + :param ACTOR_LEARNING_RATE : (float) Learning rate for the policy network + :param fresh_factor : (float) Refresh factor + :param prioritized_replay_alpha : (float) α determines how much prioritization is used + :param prioritized_replay_eps : (float) It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero + :param UPDATE_LOOPS : (int array) Number of updates of both critic and actor performed every EP_UPDATE episodes + :param save_interval : (int) save NNs interval + :param env_RL : (bool) Flag RL environment + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param MC : (bool) Flag to use MC or TD(n) + :param nsteps_TD_N : (int) Number of lookahed steps if TD(n) is used + :param UPDATE_RATE : (float) Homotopy rate to update the target critic network if TD(n) is used + :param cost_weights_terminal : (float array) Running cost weights vector + :param cost_weights_running : (float array) Terminal cost weights vector + :param dt : (float) Timestep + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param NNs_path : (str) NNs path + :param NSTEPS : (int) Max episode length + + ''' + self.env = env + self.NN = NN + self.conf = conf + + self.N_try = N_try + + self.actor_model = None + self.critic_model = None + self.target_critic = None + self.actor_optimizer = None + self.critic_optimizer = None + + self.init_rand_state = None + self.NSTEPS_SH = 0 + self.control_arr = None + self.state_arr = None + self.ee_pos_arr = None + self.exp_counter = np.zeros(conf.REPLAY_SIZE) + + return + + def setup_model(self, recover_training=None): + ''' Setup RL model ''' + # Create actor, critic and target NNs + self.actor_model = self.NN.create_actor() + + if self.conf.critic_type == 'elu': + self.critic_model = self.NN.create_critic_elu() + self.target_critic = self.NN.create_critic_elu() + elif self.conf.critic_type == 'sine': + self.critic_model = self.NN.create_critic_sine() + self.target_critic = self.NN.create_critic_sine() + elif self.conf.critic_type == 'sine-elu': + self.critic_model = self.NN.create_critic_sine_elu() + self.target_critic = self.NN.create_critic_sine_elu() + else: + self.critic_model = self.NN.create_critic_relu() + self.target_critic = self.NN.create_critic_relu() + + # Set optimizer specifying the learning rates + if self.conf.LR_SCHEDULE: + # Piecewise constant decay schedule + self.CRITIC_LR_SCHEDULE = tf.keras.optimizers.schedules.PiecewiseConstantDecay(self.conf.boundaries_schedule_LR_C, self.conf.values_schedule_LR_C) + self.ACTOR_LR_SCHEDULE = tf.keras.optimizers.schedules.PiecewiseConstantDecay(self.conf.boundaries_schedule_LR_A, self.conf.values_schedule_LR_A) + self.critic_optimizer = tf.keras.optimizers.Adam(self.CRITIC_LR_SCHEDULE) + self.actor_optimizer = tf.keras.optimizers.Adam(self.ACTOR_LR_SCHEDULE) + else: + self.critic_optimizer = tf.keras.optimizers.Adam(self.conf.CRITIC_LEARNING_RATE) + self.actor_optimizer = tf.keras.optimizers.Adam(self.conf.ACTOR_LEARNING_RATE) + + # Set initial weights of the NNs + if recover_training is not None: + NNs_path_rec = str(recover_training[0]) + N_try = recover_training[1] + update_step_counter = recover_training[2] + self.actor_model.load_weights("{}/N_try_{}/actor_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + self.critic_model.load_weights("{}/N_try_{}/critic_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + self.target_critic.load_weights("{}/N_try_{}/target_critic_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + else: + self.target_critic.set_weights(self.critic_model.get_weights()) + + def update(self, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, term_batch, weights_batch, batch_size=None): + ''' Update both critic and actor ''' + # Update the critic backpropagating the gradients + critic_grad, reward_to_go_batch, critic_value, target_critic_value = self.NN.compute_critic_grad(self.critic_model, self.target_critic, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, weights_batch) + self.critic_optimizer.apply_gradients(zip(critic_grad, self.critic_model.trainable_variables)) + + # Update the actor backpropagating the gradients + actor_grad = self.NN.compute_actor_grad(self.actor_model, self.critic_model, state_batch, term_batch, batch_size) + self.actor_optimizer.apply_gradients(zip(actor_grad, self.actor_model.trainable_variables)) + + return reward_to_go_batch, critic_value, target_critic_value + + @tf.function + def update_target(self, target_weights, weights): + ''' Update target critic NN ''' + tau = self.conf.UPDATE_RATE + for (a, b) in zip(target_weights, weights): + a.assign(b * tau + a * (1 - tau)) + + def learn_and_update(self, update_step_counter, buffer, ep): + ''' Sample experience and update buffer priorities and NNs ''' + for i in range(int(self.conf.UPDATE_LOOPS[ep])): + # Sample batch of transitions from the buffer + state_batch, partial_reward_to_go_batch, state_next_rollout_batch, dVdx_batch, d_batch, term_batch, weights_batch, batch_idxes = buffer.sample() + + # Update both critic and actor + reward_to_go_batch, critic_value, target_critic_value = self.update(state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, term_batch, weights_batch) + + # Update buffer priorities + if self.conf.prioritized_replay_alpha != 0: + buffer.update_priorities(batch_idxes, reward_to_go_batch, critic_value, target_critic_value) + + # Update target critic + if not self.conf.MC: + self.update_target(self.target_critic.variables, self.critic_model.variables) + + update_step_counter += 1 + + # Plot rollouts and save the NNs every conf.log_rollout_interval-training episodes + if update_step_counter%self.conf.save_interval == 0: + self.RL_save_weights(update_step_counter) + + return update_step_counter + + def RL_Solve(self, TO_controls, TO_states, TO_step_cost): + ''' Solve RL problem ''' + ep_return = 0 # Initialize the return + rwrd_arr = np.empty(self.NSTEPS_SH+1) # Reward array + state_next_rollout_arr = np.zeros((self.NSTEPS_SH+1, self.conf.nb_state)) # Next state array + partial_reward_to_go_arr = np.empty(self.NSTEPS_SH+1) # Partial cost-to-go array + total_reward_to_go_arr = np.empty(self.NSTEPS_SH+1) # Total cost-to-go array + term_arr = np.zeros(self.NSTEPS_SH+1) # Episode-termination flag array + term_arr[-1] = 1 + done_arr = np.zeros(self.NSTEPS_SH+1) # Episode-MC-termination flag array + + # START RL EPISODE + self.control_arr = TO_controls # action clipped in TO + + if self.conf.env_RL: + for step_counter in range(self.NSTEPS_SH): + # Simulate actions and retrieve next state and compute reward + self.state_arr[step_counter+1,:], rwrd_arr[step_counter] = self.env.step(self.conf.cost_weights_running, self.state_arr[step_counter,:], self.control_arr[step_counter,:]) + + # Compute end-effector position + self.ee_pos_arr[step_counter+1,:] = self.env.get_end_effector_position(self.state_arr[step_counter+1, :]) + rwrd_arr[-1] = self.env.reward(self.conf.cost_weights_terminal, self.state_arr[-1,:]) + else: + self.state_arr, rwrd_arr = TO_states, -TO_step_cost + + ep_return = sum(rwrd_arr) + + # Store transition after computing the (partial) cost-to go when using n-step TD (from 0 to Monte Carlo) + for i in range(self.NSTEPS_SH+1): + # set final lookahead step depending on whether Monte Cartlo or TD(n) is used + if self.conf.MC: + final_lookahead_step = self.NSTEPS_SH + done_arr[i] = 1 + else: + final_lookahead_step = min(i+self.conf.nsteps_TD_N, self.NSTEPS_SH) + if final_lookahead_step == self.NSTEPS_SH: + done_arr[i] = 1 + else: + state_next_rollout_arr[i,:] = self.state_arr[final_lookahead_step+1,:] + + # Compute the partial and total cost to go + partial_reward_to_go_arr[i] = np.float32(sum(rwrd_arr[i:final_lookahead_step+1])) + total_reward_to_go_arr[i] = np.float32(sum(rwrd_arr[i:self.NSTEPS_SH+1])) + + return self.state_arr, partial_reward_to_go_arr, total_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, self.ee_pos_arr + + def RL_save_weights(self, update_step_counter='final'): + ''' Save NN weights ''' + self.actor_model.save_weights(self.conf.NNs_path+"/N_try_{}/actor_{}.h5".format(self.N_try,update_step_counter)) + self.critic_model.save_weights(self.conf.NNs_path+"/N_try_{}/critic_{}.h5".format(self.N_try,update_step_counter)) + self.target_critic.save_weights(self.conf.NNs_path+"/N_try_{}/target_critic_{}.h5".format(self.N_try,update_step_counter)) + + def create_TO_init(self, ep, ICS): + ''' Create initial state and initial controls for TO ''' + self.init_rand_state = ICS + + self.NSTEPS_SH = self.conf.NSTEPS - int(self.init_rand_state[-1]/self.conf.dt) + if self.NSTEPS_SH == 0: + return None, None, None, None, 0 + + # Initialize array to store RL state, control, and end-effector trajectories + self.control_arr = np.empty((self.NSTEPS_SH, self.conf.nb_action)) + self.state_arr = np.empty((self.NSTEPS_SH+1, self.conf.nb_state)) + self.ee_pos_arr = np.empty((self.NSTEPS_SH+1,3)) + + # Set initial state and end-effector position + self.state_arr[0,:] = self.init_rand_state + self.ee_pos_arr[0,:] = self.env.get_end_effector_position(self.state_arr[0, :]) + + # Initialize array to initialize TO state and control variables + init_TO_controls = np.zeros((self.NSTEPS_SH, self.conf.nb_action)) + init_TO_states = np.zeros(( self.NSTEPS_SH+1, self.conf.nb_state)) + + # Set initial state + init_TO_states[0,:] = self.init_rand_state + + # Simulate actor's actions to compute the state trajectory used to initialize TO state variables (use ICS for state and 0 for control if it is the first episode otherwise use policy rollout) + success_init_flag = 1 + for i in range(self.NSTEPS_SH): + if ep == 0: + init_TO_controls[i,:] = np.zeros(self.conf.nb_action) + else: + init_TO_controls[i,:] = tf.squeeze(self.NN.eval(self.actor_model, np.array([init_TO_states[i,:]]))).numpy() + init_TO_states[i+1,:] = self.env.simulate(init_TO_states[i,:],init_TO_controls[i,:]) + if np.isnan(init_TO_states[i+1,:]).any(): + success_init_flag = 0 + return None, None, None, None, success_init_flag + + return self.init_rand_state, init_TO_states, init_TO_controls, self.NSTEPS_SH, success_init_flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_100/TO.py b/Results Single Integrator/Results set test/Code/N_try_100/TO.py new file mode 100644 index 0000000..1933f7a --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/TO.py @@ -0,0 +1,201 @@ +import sys +import math +import casadi +import numpy as np +import pinocchio.casadi as cpin + +class TO_Casadi: + + def __init__(self, env, conf, env_TO, w_S=0): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param u_min : (float array) Action lower bound array + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param dt : (float) Timestep + + :input system_id : (str) Id system + + :input w_S : (float) Sobolev-training weight + ''' + + self.env = env + self.conf = conf + + self.nx = conf.nx + self.nu = conf.na + + self.w_S = w_S + + self.CAMS = env_TO + + def TO_System_Solve(self, ICS_state, init_TO_states, init_TO_controls, T): + ''' Create and solbe TO casadi problem ''' + ### PROBLEM + opti = casadi.Opti() + + # The control models are stored as a collection of shooting nodes called running models, with an additional terminal model. + self.runningSingleModel = self.CAMS('running_model', self.conf) + runningModels = [ self.runningSingleModel for _ in range(T) ] + self.terminalModel = self.CAMS('terminal_model', self.conf) + + # Decision variables + xs = [ opti.variable(model.nx) for model in runningModels+[self.terminalModel] ] # state variable + us = [ opti.variable(model.nu) for model in runningModels ] # control variable + + # Roll out loop, summing the integral cost and defining the shooting constraints. + total_cost = 0 + + opti.subject_to(xs[0] == ICS_state[:-1]) + + for t in range(T): + x_next, r_cost = runningModels[t].step_fun(xs[t], us[t]) + opti.subject_to(xs[t + 1] == x_next ) + total_cost += r_cost + r_cost_final = self.terminalModel.cost(xs[-1], us[-1]) + total_cost += r_cost_final + + ### SOLVE + opti.minimize(total_cost) + + # Create warmstart + init_x_TO = [np.array(init_TO_states[i,:-1]) for i in range(T+1)] + init_u_TO = [np.array(init_TO_controls[i,:]) for i in range(T)] + + for x,xg in zip(xs,init_x_TO): opti.set_initial(x,xg) + for u,ug in zip(us,init_u_TO): opti.set_initial(u,ug) + + # Set solver options + opts = {'ipopt.linear_solver':'ma57', 'ipopt.sb': 'yes','ipopt.print_level': 0, 'print_time': 0} #, 'ipopt.max_iter': 500} + opti.solver("ipopt", opts) + + try: + opti.solve() + TO_states = np.array([ opti.value(x) for x in xs ]) + TO_controls = np.array([ opti.value(u) for u in us ]) + TO_total_cost = opti.value(total_cost) + TO_ee_pos_arr = np.empty((T+1,3)) + TO_step_cost = np.empty(T+1) + for n in range(T): + TO_ee_pos_arr[n,:] = np.reshape(runningModels[n].p_ee(TO_states[n,:]),-1) + TO_step_cost[n] = runningModels[n].cost(TO_states[n,:], TO_controls[n,:]) + TO_ee_pos_arr[-1,:] = np.reshape(self.terminalModel.p_ee(TO_states[-1,:]),-1) + TO_step_cost[-1] = self.terminalModel.cost(TO_states[-1,:], TO_controls[-1,:]) + success_flag = 1 + except: + print('ERROR in convergence, returning debug values') + TO_states = np.array([ opti.debug.value(x) for x in xs ]) + TO_controls = np.array([ opti.debug.value(u) for u in us ]) + TO_total_cost = None + TO_ee_pos_arr = None + TO_step_cost = None + success_flag = 0 + + return success_flag, TO_controls, TO_states, TO_ee_pos_arr, TO_total_cost, TO_step_cost + + def TO_Solve(self, ICS_state, init_TO_states, init_TO_controls, T): + ''' Retrieve TO problem solution and compute the value function derviative with respect to the state ''' + success_flag, TO_controls, TO_states, TO_ee_pos_arr, _, TO_step_cost = self.TO_System_Solve(ICS_state, init_TO_states, init_TO_controls, T) + if success_flag == 0: + return None, None, success_flag, None, None, None + + if self.w_S != 0: + # Compute V gradient w.r.t. x (no computation dV/dt) + dVdx = self.backward_pass(T+1, TO_states, TO_controls) + else: + dVdx = np.zeros((T+1, self.conf.nb_state)) + + # Add the last state component (time) + TO_states = np.concatenate((TO_states, init_TO_states[0,-1] + np.transpose(self.conf.dt*np.array([range(T+1)]))), axis=1) + + return TO_controls, TO_states, success_flag, TO_ee_pos_arr, TO_step_cost, dVdx + + def backward_pass(self, T, TO_states, TO_controls, mu=1e-9): + ''' Perform the backward-pass of DDP to obtain the derivatives of the Value function w.r.t x ''' + n = self.conf.nb_state-1 + m = self.conf.nb_action + + X_bar = np.zeros((T, n)) + for i in range(n): + X_bar[:,i] = [TO_states[t,i] for t in range(T)] + + U_bar = np.zeros((T-1, m)) + for i in range(m): + U_bar[:,i] = [TO_controls[t,i] for t in range(T-1)] + + # The task is defined by a quadratic cost: + # sum_{i=0}^T 0.5 x' l_{xx,i} x + l_{x,i} x + 0.5 u' l_{uu,i} u + l_{u,i} u + x' l_{xu,i} u + l_x = np.zeros((T, n)) + l_xx = np.zeros((T, n, n)) + l_u = np.zeros((T-1, m)) + l_uu = np.zeros((T-1, m, m)) + l_xu = np.zeros((T-1, n, m)) + + # The cost-to-go is defined by a quadratic function: 0.5 x' Q_{xx,i} x + Q_{x,i} x + ... + Q_xx = np.zeros((T-1, n, n)) + Q_x = np.zeros((T-1, n)) + Q_uu = np.zeros((T-1, m, m)) + Q_u = np.zeros((T-1, m)) + Q_xu = np.zeros((T-1, n, m)) + + x = casadi.SX.sym('x',n,1) + u = casadi.SX.sym('u',m,1) + + running_cost = -self.runningSingleModel.cost(x, u) + terminal_cost = -self.terminalModel.cost(x, u) + + running_cost_xx, running_cost_x = casadi.hessian(running_cost,x) + running_cost_uu, running_cost_u = casadi.hessian(running_cost,u) + running_cost_xu = casadi.jacobian(casadi.jacobian(running_cost,x),u) + terminal_cost_xx, terminal_cost_x = casadi.hessian(terminal_cost,x) + + fun_running_cost_x = casadi.Function('fun_running_cost_x', [x], [running_cost_x], ['x'], ['running_cost_x']) + fun_running_cost_xx = casadi.Function('fun_running_cost_xx', [x], [running_cost_xx], ['x'], ['running_cost_xx']) + fun_running_cost_xu = casadi.Function('fun_running_cost_xu', [x,u],[running_cost_xu], ['x','u'], ['running_cost_xu']) + fun_running_cost_u = casadi.Function('fun_running_cost_u', [u], [running_cost_u], ['u'], ['running_cost_u']) + fun_running_cost_uu = casadi.Function('fun_running_cost_uu', [u], [running_cost_uu], ['u'], ['running_cost_uu']) + fun_terminal_cost_x = casadi.Function('fun_terminal_cost_x', [x], [terminal_cost_x], ['x'], ['terminal_cost_x']) + fun_terminal_cost_xx = casadi.Function('fun_terminal_cost_xx',[x], [terminal_cost_xx], ['x'], ['terminal_cost_xx']) + + # The Value function is defined by a quadratic function: 0.5 x' V_{xx,i} x + V_{x,i} x + V_xx = np.zeros((T, n, n)) + V_x = np.zeros((T, n+1)) + + # Dynamics derivatives w.r.t. x and u + A = np.zeros((T-1, n, n)) + B = np.zeros((T-1, n, m)) + + # Initialize value function + l_x[-1,:], l_xx[-1,:,:] = np.reshape(fun_terminal_cost_x(X_bar[-1,:]),n), fun_terminal_cost_xx(X_bar[-1,:]) + V_xx[T-1,:,:] = l_xx[-1,:,:] + V_x[T-1,:-1] = l_x[-1,:] + + for i in range(T-2, -1, -1): + # Compute dynamics Jacobians + A[i,:,:], B[i,:,:] = self.env.augmented_derivative(X_bar[i,:], U_bar[i,:]) + + # Compute the gradient of the cost function at X=X_bar + l_x[i,:], l_xx[i,:,:] = np.reshape(fun_running_cost_x(X_bar[i,:]),n), fun_running_cost_xx(X_bar[i,:]) + l_u[i,:],l_uu[i,:,:] = np.reshape(fun_running_cost_u(U_bar[i,:]),m), fun_running_cost_uu(U_bar[i,:]) + l_xu[i,:,:] = fun_running_cost_xu(X_bar[i,:], U_bar[i,:]) + + # Compute regularized cost-to-go + Q_x[i,:] = l_x[i,:] + A[i,:,:].T @ V_x[i+1,:-1] + Q_u[i,:] = l_u[i,:] + B[i,:,:].T @ V_x[i+1,:-1] + Q_xx[i,:,:] = l_xx[i,:,:] + A[i,:,:].T @ V_xx[i+1,:,:] @ A[i,:,:] + Q_uu[i,:,:] = l_uu[i,:,:] + B[i,:,:].T @ V_xx[i+1,:,:] @ B[i,:,:] + Q_xu[i,:,:] = l_xu[i,:,:] + A[i,:,:].T @ V_xx[i+1,:,:] @ B[i,:,:] + + Qbar_uu = Q_uu[i,:,:] + mu*np.identity(m) + Qbar_uu_pinv = np.linalg.pinv(Qbar_uu) + + # Compute the derivative of the Value function w.r.t. x + V_x[i,:-1] = Q_x[i,:] - Q_xu[i,:,:] @ Qbar_uu_pinv @ Q_u[i,:] + V_xx[i,:] = Q_xx[i,:] - Q_xu[i,:,:] @ Qbar_uu_pinv @ Q_xu[i,:,:].T + + return V_x \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_100/conf_car.py b/Results Single Integrator/Results set test/Code/N_try_100/conf_car.py new file mode 100644 index 0000000..c4d72bb --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/conf_car.py @@ -0,0 +1,203 @@ +import math +import numpy as np + +system_id = 'car' + +''' CACTO parameters ''' +EP_UPDATE = 250 # Number of episodes before updating critic and actor +NUPDATES = 260000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 38000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 500 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 10000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 1e2 # Distance from target weight +w_u = 1e1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Car/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Car/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Car/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Car/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Car/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Car/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Car/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' # Link mass + +### State parameters +nb_state = 5 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 5 +na = 2 +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, -math.pi, -10, -3, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, math.pi, 10, 3, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, math.pi, 10, 3, int(NSTEPS*dt)]) # Array used to normalize states +# state: x, y, theta, v, a, t + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-10.0, 10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-10.0, -10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([12.0, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([12.0, -2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([15.0, 0.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +bound_actions = 0 +omega_lower_bound = -2 # Action lower bound +omega_upper_bound = 2 +jerk_lower_bound = -1 +jerk_upper_bound = 1 # Action upper bound +u_min = np.array([omega_lower_bound, jerk_lower_bound]) # Action lower bound vector +u_max = np.array([omega_upper_bound, jerk_upper_bound]) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_100/conf_car_park.py b/Results Single Integrator/Results set test/Code/N_try_100/conf_car_park.py new file mode 100644 index 0000000..05c66c1 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/conf_car_park.py @@ -0,0 +1,221 @@ +import math +import numpy as np + +system_id = 'car_park' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 260000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 38000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/2) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 10000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 0 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -10 # X coord center ellipse 1 +YC1 = 6.75 # Y coord center ellipse 1 +A1 = 17 # Width ellipse 1 +B1 = 4.5 # Height ellipse 1 +XC2 = 10 # X coord center ellipse 2 +YC2 = 6.75 # Y coord center ellipse 2 +A2 = 17 # Width ellipse 2 +B2 = 4.5 # Height ellipse 2 +XC3 = 0 # X coord center ellipse 2 +YC3 = -2 # Y coord center ellipse 2 +A3 = 40 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +L = 4.35 +W = 2 +L_delta = 2.63 +tau_delta = 1 + +check_points_BF = np.array([[-L/2, W/2], + [-L/2+L/3, W/2], + [-L/2+2/3*L, W/2], + [L/2, W/2], + [L/2, 0], + [L/2, -W/2], + [-L/2+2/3*L, -W/2], + [-L/2+L/3, -W/2], + [-L/2, -W/2], + [-L/2, 0]]) # Check points in BF # Check points in BF + +### Weigths +w_d = 1e2 # Distance from target weight +w_u = 1e1 # Control effort weight +w_peak = 1e6 # Target threshold weight +w_ob = 5e4 # Obstacle weight +w_v = 1e2 # Velocity weight +delta_bound = 2*np.pi/6 +w_delta_bound = 0 # Delta bound weight (not implemented) +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u, w_delta_bound]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, w_v, w_ob, w_ob, w_ob, 0, w_delta_bound]) # Terminal cost weights vector + +k_db = 50 + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 1 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = 0 # Target x position +y_des = 6.75 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Car Park/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Car Park/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Car Park/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Car Park/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Car Park/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Car Park/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Car Park/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' # Link mass + +### State parameters +nb_state = 5 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 5 +na = 2 + +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-10, 1.5, -math.pi/6, 0, 0, 0]) #1.5 #-2.5 # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 10, 3, math.pi/6, 0, 0, (NSTEPS-1)*dt]) #3.0 #10 # State upper bound initial configuration array +state_norm_arr = np.array([10, 3, math.pi, 10, math.pi/6, int(NSTEPS*dt)]) # Array used to normalize states +# state: x, y, theta, v, delta, t + +# initial configurations for plot.rollout() +init_states_sim = [np.array([-9.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-5.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-2.5 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([0.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([2.5 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([5.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([9.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +acc_lower_bound = -3 # Action lower bound +acc_upper_bound = 3 +delta_dot_lower_bound = -1 +delta_dot_upper_bound = 1 # Action upper bound +u_min = np.array([acc_lower_bound, delta_dot_lower_bound]) # Action lower bound vector +u_max = np.array([acc_upper_bound, delta_dot_upper_bound]) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-11, 11], [-2.5, 10]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_100/conf_double_integrator.py b/Results Single Integrator/Results set test/Code/N_try_100/conf_double_integrator.py new file mode 100644 index 0000000..03b56ca --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/conf_double_integrator.py @@ -0,0 +1,213 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'double_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 50000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 18000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 200 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-4 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 1 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Double Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Double Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Double Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Double Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Double Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Double Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Double Integrator/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Robot upload data +URDF_FILENAME = "double_integrator.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + +q_init, v_init = np.array([-5, 0]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max) + +### State parameters +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, dt]) # State lower bound vector +x_init_min = np.array([-15, -15, -6, -6, dt]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, 6, 6, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, 6, 6, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([12.0, 2.0, 0.0, 0.0, 0.0]), + np.array([12.0, -2.0, 0.0, 0.0, 0.0]), + np.array([15.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = robot.na # Action size +tau_lower_bound = -2 # Action lower bound +tau_upper_bound = 2 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-15, 15], [-15, 15]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_100/conf_manipulator.py b/Results Single Integrator/Results set test/Code/N_try_100/conf_manipulator.py new file mode 100644 index 0000000..6b5d539 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/conf_manipulator.py @@ -0,0 +1,225 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'manipulator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 380000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 50000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network + nsteps_TD_N = int(NSTEPS/2) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 15000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 0 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 24000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' + +NH1 = 256 # 1st hidden layer size +NH2 = 256 # 2nd hidden layer size + +LR_SCHEDULE = 1 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 1e4 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, w_v, w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 50 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -20.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Manipulator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Manipulator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Manipulator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Manipulator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Manipulator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Manipulator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Manipulator/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Robot upload data +URDF_FILENAME = "planar_manipulator_3dof.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + +q_init, v_init = np.array([math.pi, math.pi, math.pi]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max) + +### System configuration parameters +x_base = -7.0 # x coord base +y_base = 0.0 # y coord base + +### State parameters +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-math.pi, -math.pi, -math.pi, -math.pi/4, -math.pi/4, -math.pi/4, 0]) # State lower bound initial configuration vector +x_max = np.array([ np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ math.pi, math.pi, math.pi, math.pi/4, math.pi/4, math.pi/4, (NSTEPS-1)*dt]) # State upper bound initial configuration vector +state_norm_arr = np.array([15,15,15,10,10,10,int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([math.pi/4, -math.pi/8, -math.pi/8, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, math.pi/8, math.pi/8, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + #np.array([ 1.34565955, -2.39833441, 0.87800266, 0., 0., 0., 0. ]), + #np.array([-1.34565955, 2.39833441, -0.87800266, 0., 0., 0., 0. ]), + np.array([-1.55135003, 2.93707696, -1.3025857 , 0., 0., 0., 0. ]), + np.array([ 1.55135003, -2.93707696, 1.3025857 , 0., 0., 0., 0. ]), + np.array([-1.31811607, 2.63623214, -1.31811607, 0., 0., 0., 0. ]), + np.array([-0.98843209, 1.97686418, -0.98843209, 0., 0., 0., 0. ])] + #np.array([-1.05348883, 1.9057266 , -0.61849459, 0., 0., 0., 0. ])] + #np.array([ 1.05348883, -1.9057266 , 0.61849459, 0., 0., 0., 0. ])] + +### Action parameters +nb_action = robot.na # Action size +tau_lower_bound = -200 # Action lower bound +tau_upper_bound = 200 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-41, 31], [-35, 35]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_100/conf_single_integrator.py b/Results Single Integrator/Results set test/Code/N_try_100/conf_single_integrator.py new file mode 100644 index 0000000..02f767d --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/conf_single_integrator.py @@ -0,0 +1,200 @@ +import numpy as np + +system_id = 'single_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 100000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 25000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Single Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Single Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Single Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Single Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Single Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Single Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = 'set prova b' +NNs_path_rec = './Results Single Integrator/Results {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = 0 +update_step_counter_rec = 12000 + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + + + +### State parameters +nb_state = 2 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 2 +na = 2 +x_min = np.array([-np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0]), + np.array([10.0, 10.0, 0.0]), + np.array([-10.0, 10.0, 0.0]), + np.array([-10.0, -10.0, 0.0]), + np.array([12.0, 2.0, 0.0]), + np.array([12.0, -2.0, 0.0]), + np.array([15.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +tau_lower_bound = -6 # Action lower bound +tau_upper_bound = 6 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_100/conf_ur5.py b/Results Single Integrator/Results set test/Code/N_try_100/conf_ur5.py new file mode 100644 index 0000000..1521ac0 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/conf_ur5.py @@ -0,0 +1,227 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'ur5' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 380000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 50000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + # 2nd hidden layer size + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +# Obstacles parameters +XC1 = 0.0 # X coord center ellipse 1 +YC1 = 0.25 # Y coord center ellipse 1 +ZC1 = 0.2 +ell1_center = [XC1, YC1, ZC1] +A1 = 0.5 # Width ellipse 1 +B1 = 0.2 # Height ellipse 1 +C1 = 0.34 +XC2 = 0.2 # X coord center ellipse 2 +YC2 = 0.425 # Y coord center ellipse 2 +ZC2 = 0.2 +ell2_center = [XC2, YC2, ZC2] +A2 = 0.4 # Width ellipse 2 +B2 = 0.14 # Height ellipse 2 +C2 = 0.34 +XC3 = -0.2 # X coord center ellipse 2 +YC3 = 0.425 # Y coord center ellipse 2 +ZC3 = 0.2 +ell3_center = [XC3, YC3, ZC3] +A3 = 0.4 # Width ellipse 2 +B3 = 0.14 # Height ellipse 2 +C3 = 0.34 +obs_param = np.array([XC1, YC1, ZC1, XC2, YC2, ZC2, XC3, YC3, ZC3, A1, B1, C1, A2, B2, C2, A3, B3, C3]) # Obstacle parameters vector + +# Weigths +w_d = 100 # Distance from target weight +w_u = 1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +# SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +# Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +# Target parameters +x_des = 0.0 # Target x position +y_des = 0.425 # Target y position +z_des = 0.2 +TARGET_STATE = np.array([x_des, y_des, z_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results UR5/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results UR5/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results UR5/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results UR5/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results UR5/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results UR5/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results UR5/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + + + +''' System parameters ''' +# Robot upload data +URDF_FILENAME = "ur5_robot.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +# Dynamics parameters' +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' +use_viewer = False +simulate_real_time = True +show_floor = False +PRINT_T = 1 # print every PRINT_N time steps +DISPLAY_T = 0.02 # update robot configuration in viewer every DISPLAY_N time steps +# CAMERA_TRANSFORM = [3.6914889812469482, 0.4583563506603241, -0.05435386672616005, 0.48037904500961304, 0.5339481830596924, 0.5137122273445129, 0.4692920446395874] +CAMERA_TRANSFORM = [0.36461642384529114, 0.8866147994995117, 2.579286575317383, 0.03584412857890129, 0.14833784103393555, 0.9481053948402405, 0.27893954515457153] + +q_init, v_init = np.array([0.,-math.pi/2,0.,0.,0.,0.]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max, use_viewer, DISPLAY_T, CAMERA_TRANSFORM, show_floor) + +# State parameters +dt = 0.01 # Timestep +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-math.pi, -math.pi, -math.pi, -math.pi, -math.pi, -math.pi, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, 0]) # State lower bound initial configuration vector +x_max = np.array([ np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ math.pi, math.pi, math.pi, math.pi, math.pi, math.pi, math.pi/4, math.pi/4, math.pi/4, math.pi/4, math.pi/4, math.pi/4, (NSTEPS-1)*dt]) # State upper bound initial configuration vector +state_norm_arr = np.array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([math.pi/4, -math.pi/8, -math.pi/8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, math.pi/8, math.pi/8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])] + +# Action parameters +nb_action = robot.na # Action size +u_min = np.array([-150, -150, -150, -28, -28, -28]) # Action lower bound vector +u_max = np.array([150, 150, 150, 28, 28, 28]) # Action upper bound vector +w_b = 1/w_u + + +fig_ax_lim = np.array([[-3, 3], [-3, 3]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + +profile = 0 + +env_RL = 0 diff --git a/Results Single Integrator/Results set test/Code/N_try_100/environment.py b/Results Single Integrator/Results set test/Code/N_try_100/environment.py new file mode 100644 index 0000000..87bd97f --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/environment.py @@ -0,0 +1,816 @@ +import math +import mpmath +import random +import numpy as np +import tensorflow as tf +import pinocchio as pin + +from utils import * + +class Env: + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param simu : (RobotSimulator instance) + :param x_init_min : (float array) State lower bound initial configuration array + :param x_init_max : (float array) State upper bound initial configuration array + :param x_min : (float array) State lower bound vector + :param x_max : (float array) State upper bound vector + :param u_min : (float array) Action lower bound array + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + ''' + + self.conf = conf + + self.nq = conf.nq + self.nv = conf.nv + self.nx = conf.nx + self.nu = conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + def reset(self): + ''' Choose initial state uniformly at random ''' + state = np.zeros(self.conf.nb_state) + + time = random.uniform(self.conf.x_init_min[-1], self.conf.x_init_max[-1]) + for i in range(self.conf.nb_state-1): + state[i] = random.uniform(self.conf.x_init_min[i], self.conf.x_init_max[i]) + state[-1] = self.conf.dt*round(time/self.conf.dt) + + return state + + def check_ICS_feasible(self, state): + ''' Check if ICS is not feasible ''' + # check if ee is in the obstacles + p_ee = self.get_end_effector_position(state) + + ellipse1 = ((p_ee[0] - self.conf.XC1)**2) / ((self.conf.A1 / 2)**2) + ((p_ee[1] - self.conf.YC1)**2) / ((self.conf.B1 / 2)**2) + ellipse2 = ((p_ee[0] - self.conf.XC2)**2) / ((self.conf.A2 / 2)**2) + ((p_ee[1] - self.conf.YC2)**2) / ((self.conf.B2 / 2)**2) + ellipse3 = ((p_ee[0] - self.conf.XC3)**2) / ((self.conf.A3 / 2)**2) + ((p_ee[1] - self.conf.YC3)**2) / ((self.conf.B3 / 2)**2) + + feasible_flag = ellipse1 > 1 and ellipse2 > 1 and ellipse3 > 1 + + return feasible_flag + + def step(self, weights, state, action): + ''' Return next state and reward ''' + # compute next state + state_next = self.simulate(state, action) + + # compute reward + reward = self.reward(weights, state, action) + + return (state_next, reward) + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + # Simulate control action + self.conf.simu.simulate(np.copy(state[:-1]), action, self.conf.dt, 1) ### Explicit Euler ### + + # Return next state + state_next[:self.nq], state_next[self.nq:self.nx] = np.copy(self.conf.simu.q), np.copy(self.conf.simu.v) + state_next[-1] = state[-1] + self.conf.dt + + return state_next + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Create robot model in Pinocchio with q_init as initial configuration + q_init = state[:self.nq] + v_init = state[self.nq:self.nx] + + # Dynamics gradient w.r.t control (1st order euler) + pin.computeABADerivatives(self.conf.robot.model, self.conf.robot.data, np.copy(q_init), np.copy(v_init), action) + + Fu = np.zeros((self.nx+1, self.nu)) + Fu[self.nv:-1, :] = self.conf.robot.data.Minv + Fu[:self.nx, :] *= self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + q = state[:self.nq] + v = state[self.nq:self.nx] + + # Compute Jacobians for continuous time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + pin.computeABADerivatives(self.conf.robot.model, self.conf.robot.data, q, v, action) + + Fx[:self.nv, :self.nv] = 0.0 + Fx[:self.nv, self.nv:self.nx] = np.identity(self.nv) + Fx[self.nv:self.nx, :self.nv] = self.conf.robot.data.ddq_dq + Fx[self.nv:self.nx, self.nv:self.nx] = self.conf.robot.data.ddq_dv + Fu[self.nv:self.nx, :] = self.conf.robot.data.Minv + + # Convert them to discrete time + Fx = np.identity(self.conf.nb_state-1) + self.conf.dt * Fx + Fu *= self.conf.dt + + return Fx, Fu + + def simulate_batch(self, state, action): + ''' Simulate dynamics using tensors and compute its gradient w.r.t control. Batch-wise computation ''' + state_next = np.array([self.simulate(s, a) for s, a in zip(state, action)]) + + return tf.convert_to_tensor(state_next, dtype=tf.float32) + + def derivative_batch(self, state, action): + ''' Simulate dynamics using tensors and compute its gradient w.r.t control. Batch-wise computation ''' + Fu = np.array([self.derivative(s, a) for s, a in zip(state, action)]) + + return tf.convert_to_tensor(Fu, dtype=tf.float32) + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + q = state[:self.nq] + + RF = self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id) + + H = self.conf.robot.framePlacement(q, RF, recompute) + + p = H.translation + + return p + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + +class SingleIntegrator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[0,0] = self.conf.dt + Fu[1,1] = self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0], + [0, 1]]) + + Fu[0,0] = self.conf.dt + Fu[1,1] = self.conf.dt + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*action[0] + state_next[1] = state[1] + self.conf.dt*action[1] + state_next[2] = state[2] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + + return p + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class DoubleIntegrator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = [self.get_end_effector_position(state)[i] for i in range(2)] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = np.math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class Car(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[2,0] = self.conf.dt + Fu[4,1] = self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0, -self.conf.dt*state[3]*math.sin(state[2]) -self.conf.dt**2*state[4]*math.sin(state[2])/2, self.conf.dt*math.cos(state[2]), self.conf.dt**2*math.cos(state[2])/2], + [0, 1, self.conf.dt*state[3]*math.cos(state[2]) +self.conf.dt**2*state[4]*math.cos(state[2])/2, self.conf.dt*math.sin(state[2]), self.conf.dt**2*math.sin(state[2])/2], + [0, 0, 1, 0, 0], + [0, 0, 0, 1, self.conf.dt], + [0, 0, 0, 0, 1]]) + + Fu[2,0] = self.conf.dt + Fu[4,1] = self.conf.dt + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*state[3]*tf.cos(state[2]) + self.conf.dt**2*state[4]*tf.cos(state[2])/2 + state_next[1] = state[1] + self.conf.dt*state[3]*tf.sin(state[2]) + self.conf.dt**2*state[4]*tf.sin(state[2])/2 + state_next[2] = state[2] + self.conf.dt*action[0] + state_next[3] = state[3] + self.conf.dt*state[4] + state_next[4] = state[4] + self.conf.dt*action[1] + state_next[5] = state[5] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + + return p + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class CarPark(Car): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def check_ICS_feasible(self, state): + ''' Check if ICS is not feasible ''' + # check if ee is in the obstacles + x_ee, y_ee = self.get_end_effector_position(state)[:2] + theta_ee = state[2] + + for i in range(len(self.conf.check_points_BF)): + check_points_WF_i = np.array([x_ee, y_ee]) + np.array([[math.cos(theta_ee), -math.sin(theta_ee)], [math.sin(theta_ee), math.cos(theta_ee)]]).dot(self.conf.check_points_BF[i,:]) + obs_1 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC1,self.YC1,self.A1,self.B1) + obs_2 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC2,self.YC2,self.A2,self.B2) + obs_3 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC3,self.YC3,self.A3,self.B3) + + feasible_flag = obs_1 < 0.5 and obs_2 < 0.5 and obs_3 < 0.5 + if feasible_flag == 0: + return feasible_flag + + return feasible_flag + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[3,0] = self.conf.dt + Fu[4,1] = self.conf.dt/self.conf.tau_delta + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0, -self.conf.dt*state[3]*math.sin(state[2]), self.conf.dt*math.cos(state[2]), 0], + [0, 1, self.conf.dt*state[3]*math.cos(state[2]), self.conf.dt*math.sin(state[2]), 0], + [0, 0, 1, self.conf.dt*math.tan(state[4])/self.conf.L_delta, self.conf.dt*state[3]*mpmath.sec(state[4])**2/self.conf.L_delta], + [0, 0, 0, 1, 0], + [0, 0, 0, 0, 1]]) + + Fu[3,0] = self.conf.dt + Fu[4,1] = self.conf.dt/self.conf.tau_delta + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*state[3]*math.cos(state[2]) + state_next[1] = state[1] + self.conf.dt*state[3]*math.sin(state[2]) + state_next[2] = state[2] + self.conf.dt*state[3]*math.tan(state[4])/self.conf.L_delta + state_next[3] = state[3] + self.conf.dt*action[0] + state_next[4] = state[4] + self.conf.dt*action[1]/self.conf.tau_delta + state_next[5] = state[5] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + np.array([[math.cos(state[2]), -math.sin(state[2])], [math.sin(state[2]), math.cos(state[2])]]).dot(np.array([self.conf.L_delta/2,0])) + + return p + + def obs_cost_fun(self,x,y,x_step,y_step,Wx,Wy,fv=1,k=50): + k = self.conf.k_db + + term1 = 4 + 4 * (y - y_step + Wy/2)**2 * k**2 + term2 = 4 + 4 * (y - y_step - Wy/2)**2 * k**2 + term3 = 4 + 4 * (x - x_step + Wx/2)**2 * k**2 + term4 = 4 + 4 * (x - x_step - Wx/2)**2 * k**2 + obs_cost = (term1)**(-1/2) * fv * (-np.sqrt(term2) / 2 + (y - y_step - Wy/2) * k) * (term3)**(-1/2) * (term2)**(-1/2) * (np.sqrt(term1) / 2 + (y - y_step + Wy/2) * k) * (term4)**(-1/2) * (np.sqrt(term3) / 2 + (x - x_step + Wx/2) * k) * (-np.sqrt(term4) / 2 + (x - x_step - Wx/2) * k) + + return obs_cost + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + theta_ee = state[2] + + obs_cost = 0 + check_points_WF = np.dot(np.array([[np.cos(theta_ee), -np.sin(theta_ee)], [np.sin(theta_ee), np.cos(theta_ee)]]), self.conf.check_points_BF.T).T + np.array([x_ee, y_ee]) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC1, self.YC1, self.A1, self.B1)) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC2, self.YC2, self.A2, self.B2)) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC3, self.YC3, self.A3, self.B3)) + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 + 0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 + 0.1) - math.sqrt(0.1) - 0.1 )) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*state[3]**2 - weights[3]*obs_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class Manipulator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = [self.get_end_effector_position(state)[i] for i in range(2)] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term penalizing the FINAL joint velocity + if weights[2] != 0: + vel_cost = state[self.nq:self.nx].dot(state[self.nq:self.nx]) + else: + vel_cost = 0 + + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*vel_cost - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) #- weights[2]*vel_cost + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class UR5(Env): + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.ZC1 = self.conf.obs_param[2] + self.XC2 = self.conf.obs_param[3] + self.YC2 = self.conf.obs_param[4] + self.ZC2 = self.conf.obs_param[5] + self.XC3 = self.conf.obs_param[6] + self.YC3 = self.conf.obs_param[7] + self.ZC3 = self.conf.obs_param[8] + + self.A1 = self.conf.obs_param[9] + self.B1 = self.conf.obs_param[10] + self.C1 = self.conf.obs_param[11] + self.A2 = self.conf.obs_param[12] + self.B2 = self.conf.obs_param[13] + self.C2 = self.conf.obs_param[14] + self.A3 = self.conf.obs_param[15] + self.B3 = self.conf.obs_param[16] + self.C3 = self.conf.obs_param[17] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee, z_ee = self.get_end_effector_position(state) + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) + ((z_ee-self.ZC1)**2)/((self.C1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) + ((z_ee-self.ZC2)**2)/((self.C2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) + ((z_ee-self.ZC3)**2)/((self.C3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((z_ee-self.TARGET_STATE[2])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + if action is not None: + u_cost = action.dot(action) + else: + u_cost = 0 + + # Term penalizing the FINAL joint velocity + vel_cost = state[self.nq:self.nx].dot(state[self.nq:self.nx]) + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + (z_ee-self.TARGET_STATE[2])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*vel_cost - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) diff --git a/Results Single Integrator/Results set test/Code/N_try_100/environment_TO.py b/Results Single Integrator/Results set test/Code/N_try_100/environment_TO.py new file mode 100644 index 0000000..0cf4b63 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/environment_TO.py @@ -0,0 +1,778 @@ +import sys +import math +import casadi +import numpy as np +import pinocchio.casadi as cpin + +class SingleIntegrator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + p_ee[-1] = 0 + + return p_ee + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*u[0] + x_next[1] = x[1] + self.conf.dt*u[1] + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class DoubleIntegrator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class Car_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + p_ee[-1] = 0 + + return p_ee + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*x[3]*casadi.cos(x[2]) + self.conf.dt**2*x[4]*casadi.cos(x[2])/2 + x_next[1] = x[1] + self.conf.dt*x[3]*casadi.sin(x[2]) + self.conf.dt**2*x[4]*casadi.sin(x[2])/2 + x_next[2] = x[2] + self.conf.dt*u[0] + x_next[3] = x[3] + self.conf.dt*x[4] + x_next[4] = x[4] + self.conf.dt*u[1] + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class CarPark_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + np.array([[np.cos(cx[2]), -np.sin(cx[2])], [np.sin(cx[2]), np.cos(cx[2])]]).dot(np.array([self.conf.L_delta/2,0])) + p_ee[-1] = 0 + + return p_ee + + def obs_cost_fun(self, x, y, x_clip, y_clip, Wx, Wy, fv=1, k=50): + k = self.conf.k_db + obs_cost = (4 + 4 * (y - y_clip + Wy / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * fv * (-np.sqrt(4 + 4 * (y - y_clip - Wy / 2) ** 2 * k ** 2) / 2 + (y - y_clip - Wy / 2) * k) * (4 + 4 * (x - x_clip + Wx / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (4 + 4 * (y - y_clip - Wy / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (np.sqrt(4 + 4 * (y - y_clip + Wy / 2) ** 2 * k ** 2) / 2 + (y - y_clip + Wy / 2) * k) * (4 + 4 * (x - x_clip - Wx / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (np.sqrt(4 + 4 * (x - x_clip + Wx / 2) ** 2 * k ** 2) / 2 + (x - x_clip + Wx / 2) * k) * (-np.sqrt(4 + 4 * (x - x_clip - Wx / 2) ** 2 * k ** 2) / 2 + (x - x_clip - Wx / 2) * k) + + return obs_cost + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def rotation_matrix(self, angle): + ''' Compute the 2x2 rotation matrix for a given angle ''' + cos_theta = casadi.cos(angle) + sin_theta = casadi.sin(angle) + + rotation_mat = casadi.vertcat(casadi.horzcat(cos_theta, -sin_theta), + casadi.horzcat(sin_theta, cos_theta)) + return rotation_mat + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee_tmp = self.p_ee(x) + p_ee = p_ee_tmp[:2] + theta_ee = x[2] + + ### Penalties representing the obstacle ### + obs_cost = 0 + check_points_WF = casadi.mtimes(self.rotation_matrix(theta_ee), self.conf.check_points_BF.T).T + casadi.repmat(p_ee.T, self.conf.check_points_BF.shape[0], 1) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC1, self.YC1, self.A1, self.B1)) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC2, self.YC2, self.A2, self.B2)) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC3, self.YC3, self.A3, self.B3)) + + ### Control effort term ### + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 2*0.1 - 2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*x[3]**2 + self.weights[3]*obs_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*x[3]*casadi.cos(x[2]) + x_next[1] = x[1] + self.conf.dt*x[3]*casadi.sin(x[2]) + x_next[2] = x[2] + self.conf.dt*x[3]*casadi.tan(x[4])/self.conf.L_delta + x_next[3] = x[3] + self.conf.dt*u[0] + x_next[4] = x[4] + self.conf.dt*u[1]/self.conf.tau_delta + + return x_next + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class Manipulator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class UR5_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.ZC1 = self.conf.obs_param[2] + self.XC2 = self.conf.obs_param[3] + self.YC2 = self.conf.obs_param[4] + self.ZC2 = self.conf.obs_param[5] + self.XC3 = self.conf.obs_param[6] + self.YC3 = self.conf.obs_param[7] + self.ZC3 = self.conf.obs_param[8] + + self.A1 = self.conf.obs_param[9] + self.B1 = self.conf.obs_param[10] + self.C1 = self.conf.obs_param[11] + self.A2 = self.conf.obs_param[12] + self.B2 = self.conf.obs_param[13] + self.C2 = self.conf.obs_param[14] + self.A3 = self.conf.obs_param[15] + self.B3 = self.conf.obs_param[16] + self.C3 = self.conf.obs_param[17] + + self.TARGET_STATE = self.conf.TARGET_STATE + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC1)**2)/((self.conf.A1/2)**2) + ((p_ee[1]-self.conf.YC1)**2)/((self.conf.B1/2)**2) + ((p_ee[2]-self.conf.ZC1)**2)/((self.conf.C1/2)**2) - 1.0)) + 1)/self.conf.alpha + ell2_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC2)**2)/((self.conf.A2/2)**2) + ((p_ee[1]-self.conf.YC2)**2)/((self.conf.B2/2)**2) + ((p_ee[2]-self.conf.ZC2)**2)/((self.conf.C2/2)**2) - 1.0)) + 1)/self.conf.alpha + ell3_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC3)**2)/((self.conf.A3/2)**2) + ((p_ee[1]-self.conf.YC3)**2)/((self.conf.B3/2)**2) + ((p_ee[2]-self.conf.ZC3)**2)/((self.conf.C3/2)**2) - 1.0)) + 1)/self.conf.alpha + + ### Control effort term ### + u_cost = 0 + for i in range(self.nu): + u_cost += u[i]**2 + self.conf.w_b*(u[i]/self.conf.u_max[i])**10 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.conf.alpha2*-(np.sqrt((p_ee[0]-self.conf.TARGET_STATE[0])**2 +0.1) - np.sqrt(0.1) - 0.1 + np.sqrt((p_ee[1]-self.conf.TARGET_STATE[1])**2 +0.1) - np.sqrt(0.1) - 0.1 + np.sqrt((p_ee[2]-self.conf.TARGET_STATE[2])**2 +0.1) - np.sqrt(0.1) - 0.1)) + 1)/self.conf.alpha2 + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.conf.TARGET_STATE[0])**2 + (p_ee[1]-self.conf.TARGET_STATE[1])**2 + (p_ee[2]-self.conf.TARGET_STATE[2])**2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_100/generate_tests_set_script.py b/Results Single Integrator/Results set test/Code/N_try_100/generate_tests_set_script.py new file mode 100644 index 0000000..4c5f781 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/generate_tests_set_script.py @@ -0,0 +1,26 @@ +### Create file to be executed ### +import os +import shutil + +system_id = 'car' +system_id_short = 'C' + +n_runs = 10 + +w_S = 1e-3 +info = '{}'.format(1) + +offset = 0 if w_S ==0 else 100 + +seed_list = [29556, 5280, 739, 92, 10, 7298, 14, 264, 22135, 342] + +shutil.copy('Template.sh', '{}{}.sh'.format(system_id_short,info)) +with open('{}{}.sh'.format(system_id_short,info), 'a') as f: + for i in range(1,1 + n_runs): + f.write("nohup python3 -u main.py --system-id='{}' --test-n={} --seed={} --w-S={} > out/{}{}.txt &\n".format(system_id,offset+i,seed_list[i-1], w_S, system_id_short,offset+i)) + f.write("\necho 'Running scripts in parallel'\n") + f.write("echo 'PID of this script is:' $$\n") + f.write("wait\n") + f.write("echo 'Script done running'\n") + +os.makedirs('out', exist_ok=True) \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_100/main.py b/Results Single Integrator/Results set test/Code/N_try_100/main.py new file mode 100644 index 0000000..8121e8c --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/main.py @@ -0,0 +1,254 @@ +import os +import sys +import time +import shutil +import random +import argparse +import importlib +import numpy as np +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # {'0' -> show all logs, '1' -> filter out info, '2' -> filter out warnings} +import tensorflow as tf +from multiprocessing import Pool +from RL import RL_AC +from TO import TO_Casadi +from plot_utils import PLOT +from NeuralNetwork import NN +from replay_buffer import PrioritizedReplayBuffer, ReplayBuffer + +def parse_args(): + ''' Parse the arguments for CACTO training ''' + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + + parser.add_argument('--test-n', type=int, default=0, + help="Test number") + + parser.add_argument('--seed', type=int, default=0, + help="random and tf.random seed") + + parser.add_argument('--system-id', type=str, default='single_integrator', + choices=["single_integrator", "double_integrator", "car", "car_park", "manipulator", "ur5"], + help="System-id (single_integrator, double_integrator, car, manipulator, ur5") + + parser.add_argument('--recover-training-flag', type=bool, default=False, + choices=["True", "False"], + help="Flag to recover training") + ### Not tested ### + #parser.add_argument('--GPU-flag', type=bool, default=False, + # choices=["True", "False"], + # help="Flag to use GPU") + + parser.add_argument('--nb-cpus', type=int, default=10, + help="Number of TO problems solved in parallel") + + parser.add_argument('--w-S', type=float, default=0, + help="Sobolev training - weight of the value related error") + + args = parser.parse_args() + dict_args = vars(args) + + return dict_args + + + + +if __name__ == '__main__': + + args = parse_args() + + ############### Input ############### + N_try = args['test_n'] + + if args['seed'] == None: + seed = random.randint(1,100000) + else: + seed = args['seed'] + tf.random.set_seed(seed) # Set tensorflow seed + random.seed(seed) # Set random seed + + system_id = args['system_id'] + + recover_training_flag = args['recover_training_flag'] + + ### Not tested ### + #GPU_flag = args['GPU_flag'] + #if GPU_flag: + # os.environ["CUDA_VISIBLE_DEVICES"]="-1" + #print(tf.config.experimental.list_physical_devices('GPU')) + + nb_cpus = args['nb_cpus'] + + w_S = args['w_S'] + ######################################################### + + + + # Import configuration file and environment file + system_map = { + 'single_integrator': ('conf_single_integrator', 'SingleIntegrator', 'SingleIntegrator_CAMS'), + 'double_integrator': ('conf_double_integrator', 'DoubleIntegrator', 'DoubleIntegrator_CAMS'), + 'car': ('conf_car', 'Car', 'Car_CAMS'), + 'car_park': ('conf_car_park', 'CarPark', 'CarPark_CAMS'), + 'manipulator': ('conf_manipulator', 'Manipulator', 'Manipulator_CAMS'), + 'ur5': ('conf_ur5', 'UR5', 'UR5_CAMS') + } + try: + conf_module, env_class, env_TO_class = system_map[system_id] + conf = importlib.import_module(conf_module) + Environment = getattr(importlib.import_module('environment'), env_class) + Environment_TO = getattr(importlib.import_module('environment_TO'), env_TO_class) + except KeyError: + print('System {} not found'.format(system_id)) + sys.exit() + + # Create folders to store the results and the trained NNs + for path in conf.path_list: + os.makedirs(path + '/N_try_{}'.format(N_try), exist_ok=True) + os.makedirs(conf.Config_path, exist_ok=True) + + # Save configuration + params = [p for p in conf.__dict__ if not p.startswith("__")] + with open(conf.Config_path + '/config{}.txt'.format(N_try), 'w') as f: + for p in params: + f.write('{} = {}\n'.format(p, conf.__dict__[p])) + f.write('Seed = {}\n'.format(seed)) + f.write('w_S = {}'.format(w_S)) + + shutil.copy('{}.py'.format(conf_module), conf.Config_path + '/' + conf_module + '_{}.py'.format(N_try)) + with open(conf.Config_path + '/' + conf_module + '_{}.py'.format(N_try), 'a') as f: + f.write('\n\n# {}'.format(args)) + + # Copy all file with .py extension from /mydir to /mydestdir + for file in os.listdir("./"): + if file.endswith(".py"): + shutil.copy(os.path.join("./", file), os.path.join(conf.Code_path + '/N_try_{}'.format(N_try), file)) + + # Create empty txt file in Log_path to store the test info + open(conf.Log_path + '/info.txt', 'a').close() + + + + ### Create instances of the used classes ### + env = Environment(conf) # Create environment instances + env_TO = Environment_TO + NN_inst = NN(env, conf, w_S) # Create NN instance + TrOp = TO_Casadi(env, conf, env_TO, w_S) # Create TO instance + RLAC = RL_AC(env, NN_inst, conf, N_try) # Create RL instance + buffer = ReplayBuffer(conf) if conf.prioritized_replay_alpha == 0 else PrioritizedReplayBuffer(conf) # Create an empty (prioritized) replay buffer + plot_fun = PLOT(N_try, env, NN_inst, conf) # Create PLOT instance + + # Set initial weights of the NNs, initialize the counter of the updates and setup NN models + if recover_training_flag: + recover_training = np.array([conf.NNs_path_rec, conf.N_try_rec, conf.update_step_counter_rec]) + update_step_counter = conf.update_step_counter_rec + + RLAC.setup_model(recover_training) + else: + update_step_counter = 0 + + RLAC.setup_model() + + # Save initial weights of the NNs + RLAC.RL_save_weights(update_step_counter) + + # Plot initial rollouts + plot_fun.plot_traj_from_ICS(np.array(conf.init_states_sim), TrOp, RLAC, update_step_counter=update_step_counter,steps=conf.NSTEPS, init=0) + + # Initialize arrays to store the reward history of each episode and the average reward history of last 100 episodes + ep_arr_idx = 0 + ep_reward_arr = np.zeros(conf.NEPISODES-ep_arr_idx)*np.nan + + def compute_sample(args): + ''' Create samples solving TO problems starting from given ICS ''' + ep = args[0] + ICS = args[1] + + # Create initial TO # + init_rand_state, init_TO_states, init_TO_controls, NSTEPS_SH, success_init_flag = RLAC.create_TO_init(ep, ICS) + if success_init_flag == 0: + return None + + # Solve TO problem # + TO_controls, TO_states, success_flag, TO_ee_pos_arr, TO_step_cost, dVdx = TrOp.TO_Solve(init_rand_state, init_TO_states, init_TO_controls, NSTEPS_SH) + if success_flag == 0: + return None + + # Collect experiences + state_arr, partial_reward_to_go_arr, total_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, RL_ee_pos_arr = RLAC.RL_Solve(TO_controls, TO_states, TO_step_cost) + + if conf.env_RL == 0: + RL_ee_pos_arr = TO_ee_pos_arr + + return NSTEPS_SH, TO_controls, TO_ee_pos_arr, dVdx, state_arr.tolist(), partial_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, RL_ee_pos_arr + + def create_unif_TO_init(n_UICS=1): + ''' Create n uniformely distributed ICS ''' + # Create ICS TO # + init_rand_state = env.reset() + + return init_rand_state + + + + ### START TRAINING ### + if conf.profile: + import cProfile, pstats + + profiler = cProfile.Profile() + profiler.enable() + + time_start = time.time() + + for ep in range(conf.NLOOPS): + # Generate and store conf.EP_UPDATE random-uniform ICS + with Pool(nb_cpus) as p: + init_rand_state = p.map(create_unif_TO_init, range(conf.EP_UPDATE)) + + # Generate samples + with Pool(nb_cpus) as p: + tmp = p.map(compute_sample, zip(ep*np.ones(conf.EP_UPDATE), init_rand_state)) + + # Remove unsuccessful TO problems and update EP_UPDATE + tmp = [x for x in tmp if x is not None] + NSTEPS_SH, TO_controls, ee_pos_arr_TO, dVdx, state_arr, partial_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, ee_pos_arr_RL = zip(*tmp) + + # Update the buffer + buffer.add(state_arr, partial_reward_to_go_arr, state_next_rollout_arr, dVdx, done_arr, term_arr) + + # Update NNs + update_step_counter = RLAC.learn_and_update(update_step_counter, buffer, ep) + + # plot Critic value function + #plot_fun.plot_Critic_Value_function(RLAC.critic_model, update_step_counter, system_id) ### + + # Plot rollouts and state and control trajectories + if update_step_counter%conf.plot_rollout_interval_diff_loc == 0 or system_id == 'single_integrator' or system_id == 'double_integrator' or system_id == 'car_park' or system_id == 'car' or system_id == 'manipulator': + print("System: {} - N_try = {}".format(conf.system_id, N_try)) + plot_fun.plot_Critic_Value_function(RLAC.critic_model, update_step_counter, system_id) + plot_fun.plot_traj_from_ICS(np.array(conf.init_states_sim), TrOp, RLAC, update_step_counter=update_step_counter, ep=ep,steps=conf.NSTEPS, init=1) + + # Update arrays to store the reward history and its average + ep_reward_arr[ep_arr_idx:ep_arr_idx+len(tmp)] = ep_return + ep_arr_idx += len(tmp) + + for i in range(len(tmp)): + print("Episode {} ---> Return = {}".format(ep*len(tmp) + i, ep_return[i])) + + if update_step_counter > conf.NUPDATES: + break + + time_end = time.time() + print('Elapsed time: ', time_end-time_start) + + if conf.profile: + profiler.disable() + stats = pstats.Stats(profiler).sort_stats('cumtime') + stats.print_stats() + + # Plot returns + plot_fun.plot_Return(ep_reward_arr) + + # Save networks at the end of the training + RLAC.RL_save_weights() + + # Simulate the final policy + plot_fun.rollout(update_step_counter, RLAC.actor_model, conf.init_states_sim) diff --git a/Results Single Integrator/Results set test/Code/N_try_100/plot_utils.py b/Results Single Integrator/Results set test/Code/N_try_100/plot_utils.py new file mode 100644 index 0000000..55daab0 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/plot_utils.py @@ -0,0 +1,714 @@ +import math +import numpy as np +import tensorflow as tf +import matplotlib.pyplot as plt +from matplotlib import cm, colors +from matplotlib.patches import Ellipse, FancyBboxPatch, Rectangle +from matplotlib.transforms import Affine2D +import mpl_toolkits.mplot3d.art3d as art3d + +class PLOT(): + def __init__(self, N_try, env, NN, conf): + ''' + :input N_try : (Test number) + + :input env : (Environment instance) + + :input conf : (Configuration file) + :param fig_ax_lim : (float array) Figure axis limit [x_min, x_max, y_min, y_max] + :param Fig_path : (str) Figure path + :param NSTEPS : (int) Max episode length + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param NORMALIZE_INPUTS : (bool) Flag to normalize inputs (state) + :param state_norm_array : (float array) Array used to normalize states + :param dt : (float) Timestep + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + ''' + self.env = env + self.NN = NN + self.conf = conf + + self.N_try = N_try + + self.xlim = conf.fig_ax_lim[0].tolist() + self.ylim = conf.fig_ax_lim[1].tolist() + + # Set the ticklabel font size globally + plt.rcParams['xtick.labelsize'] = 22 + plt.rcParams['ytick.labelsize'] = 22 + plt.rcParams.update({'font.size': 20}) + + return + + def plot_obstaces(self, a=1): + if self.conf.system_id == 'car_park': + obs1 = Rectangle((self.conf.XC1-self.conf.A1/2, self.conf.YC1-self.conf.B1/2), self.conf.A1, self.conf.B1, 0.0,alpha=a) + obs1.set_facecolor([30/255, 130/255, 76/255, 1]) + obs2 = Rectangle((self.conf.XC2-self.conf.A2/2, self.conf.YC2-self.conf.B2/2), self.conf.A2, self.conf.B2, 0.0,alpha=a) + obs2.set_facecolor([30/255, 130/255, 76/255, 1]) + obs3 = Rectangle((self.conf.XC3-self.conf.A3/2, self.conf.YC3-self.conf.B3/2), self.conf.A3, self.conf.B3, 0.0,alpha=a) + obs3.set_facecolor([30/255, 130/255, 76/255, 1]) + + #rec1 = FancyBboxPatch((self.conf.XC1-self.conf.A1/2, self.conf.YC1-self.conf.B1/2), self.conf.A1, self.conf.B1,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec1.set_facecolor([30/255, 130/255, 76/255, 1]) + #rec2 = FancyBboxPatch((self.conf.XC2-self.conf.A2/2, self.conf.YC2-self.conf.B2/2), self.conf.A2, self.conf.B2,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec2.set_facecolor([30/255, 130/255, 76/255, 1]) + #rec3 = FancyBboxPatch((self.conf.XC3-self.conf.A3/2, self.conf.YC3-self.conf.B3/2), self.conf.A3, self.conf.B3,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec3.set_facecolor([30/255, 130/255, 76/255, 1]) + else: + obs1 = Ellipse((self.conf.XC1, self.conf.YC1), self.conf.A1, self.conf.B1, 0.0,alpha=a) + obs1.set_facecolor([30/255, 130/255, 76/255, 1]) + obs2 = Ellipse((self.conf.XC2, self.conf.YC2), self.conf.A2, self.conf.B2, 0.0,alpha=a) + obs2.set_facecolor([30/255, 130/255, 76/255, 1]) + obs3 = Ellipse((self.conf.XC3, self.conf.YC3), self.conf.A3, self.conf.B3, 0.0,alpha=a) + obs3.set_facecolor([30/255, 130/255, 76/255, 1]) + + return [obs1, obs2, obs3] + + def plot_Reward(self, plot_obs=0): + x = np.arange(-15, 15, 0.1) + y = np.arange(-10, 10, 0.1) + theta = np.pi/2 + ICS = np.array([np.array([i,j,0]) for i in x for j in y]) + state = np.array([self.compute_ICS(np.array([i,j,0]), 'car')[0] for i in x for j in y]) # for k in theta] + state[:,2] = theta + r = [self.env.reward(self.conf.cost_weights_running, s) for s in state] + mi = min(r) + ma = max(r) + norm = colors.Normalize(vmin=mi,vmax=ma) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + pti = ax.scatter(ICS[:,0], ICS[:,1], norm=norm, c=r, cmap=cm.get_cmap('hot_r')) + plt.colorbar(pti) + + if plot_obs: + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + + # Center and check points of 'car_park' system + #check_points_WF_i = np.array([[math.cos(theta), -math.sin(theta)], [math.sin(theta), math.cos(theta)]]).dot(self.conf.check_points_BF[0,:]) + ICS[0,:2] + #ax.scatter(check_points_WF_i[0], check_points_WF_i[1], c='b') + #for i in range(1,len(self.conf.check_points_BF)): + # check_points_WF_i = np.array([[math.cos(theta), -math.sin(theta)], [math.sin(theta), math.cos(theta)]]).dot(self.conf.check_points_BF[i,:]) + ICS[0,:2] + # ax.scatter(check_points_WF_i[0], check_points_WF_i[1], c='r') + + ax.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5, legend='Goal position') + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.set_title('Plane') + #ax.legend() + ax.grid(True) + plt.show() + + def compute_ICS(self, p_ee, sys_id, theta=None, continue_flag=0): + if sys_id == 'manipulator': + radius = math.sqrt((p_ee[0]-self.conf.x_base)**2+(p_ee[1])**2) + if radius > 30: + continue_flag = 1 + return None, continue_flag + + phi = math.atan2(p_ee[1]-self.conf.y_base,(p_ee[0]-self.conf.x_base)) # SUM OF THE ANGLES FIXED + X3rd_joint = (p_ee[0]-self.conf.x_base) - self.conf.l* math.cos(phi) + Y3rd_joint = (p_ee[1]-self.conf.y_base) - self.conf.l* math.sin(phi) + + if abs(X3rd_joint) <= 1e-6 and abs(Y3rd_joint) <= 1e-6: + continue_flag = 1 + return None, continue_flag + + c2 = (X3rd_joint**2 + Y3rd_joint**2 -2*self.conf.l**2)/(2*self.conf.l**2) + + if p_ee[1] >= 0: + s2 = math.sqrt(1-c2**2) + else: + s2 = -math.sqrt(1-c2**2) + + s1 = ((self.conf.l + self.conf.l*c2)*Y3rd_joint - self.conf.l*s2*X3rd_joint)/(X3rd_joint**2 + Y3rd_joint**2) + c1 = ((self.conf.l + self.conf.l*c2)*X3rd_joint - self.conf.l*s2*Y3rd_joint)/(X3rd_joint**2 + Y3rd_joint**2) + ICS_q0 = math.atan2(s1,c1) + ICS_q1 = math.atan2(s2,c2) + ICS_q2 = phi-ICS_q0-ICS_q1 + + ICS = np.array([ICS_q0, ICS_q1, ICS_q2, 0.0, 0.0, 0.0, 0.0]) + + elif sys_id == 'car': + if theta == None: + theta = 0*np.random.uniform(-math.pi,math.pi) + ICS = np.array([p_ee[0], p_ee[1], theta, 0.0, 0.0, 0.0]) + + elif sys_id == 'car_park': + if theta == None: + #theta = 0*np.random.uniform(-math.pi,math.pi) + theta = np.pi/2 + ICS = np.array([p_ee[0], p_ee[1], theta, 0.0, 0.0, 0.0]) + + elif sys_id == 'double_integrator': + ICS = np.array([p_ee[0], p_ee[1], 0.0, 0.0, 0.0]) + + elif sys_id == 'single_integrator': + ICS = np.array([p_ee[0], p_ee[1], 0.0]) + + return ICS, continue_flag + + def plot_policy(self, tau, x, y, steps, n_updates, diff_loc=0): + ''' Plot policy rollout from a single initial state as well as state and control trajectories ''' + timesteps = self.self.conf.dt*np.arange(steps) + + fig = plt.figure(figsize=(12,8)) + plt.suptitle('POLICY: Discrete model, N try = {} N updates = {}'.format(self.N_try,n_updates), y=1) + + ax1 = fig.add_subplot(2, 2, 1) + ax1.plot(timesteps, x, 'ro', linewidth=1, markersize=1, legedn='x') + ax1.plot(timesteps, y, 'bo', linewidth=1, markersize=1, legend='y') + ax1.set_xlabel('Time [s]') + ax1.set_ylabel('[m]') + ax1.set_title('End-Effector Position') + ax1.legend() + ax1.grid(True) + + col = ['ro', 'bo', 'go'] + ax2 = fig.add_subplot(2, 2, self.conf.nb_action) + for i in range(self.conf.nb_action): + ax2.plot(timesteps, tau[:,i], col[i], linewidth=1, markersize=1,legend='tau{}'.format(i)) + ax2.set_xlabel('Time [s]') + ax2.set_title('Controls') + ax2.legend() + ax2.grid(True) + + ax3 = fig.add_subplot(1, 2, 2) + ax3.plot(x, y, 'ro', linewidth=1, markersize=1) + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax3.add_artist(obs_plot_list[i]) + ax3.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=10) + ax3.set_xlim(self.xlim) + ax3.set_ylim(self.ylim) + ax3.set_aspect('equal', 'box') + ax3.set_xlabel('X [m]') + ax3.set_ylabel('Y [m]') + ax3.set_title('Plane') + ax3.grid(True) + + fig.tight_layout() + + if diff_loc==0: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationSingleInit_{}_{}'.format(self.N_try,n_updates)) + else: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationMultiInit_{}_{}'.format(self.N_try,n_updates)) + + plt.clf() + plt.close(fig) + + def plot_policy_eval(self, p_list, n_updates, diff_loc=0, theta=0): + ''' Plot only policy rollouts from multiple initial states ''' + fig = plt.figure(figsize=(12,8)) + plt.suptitle('POLICY: Discrete model, N try = {} N updates = {}'.format(self.N_try,n_updates), y=1) + + ax = fig.add_subplot(1, 1, 1) + for idx in range(len(p_list)): + ax.plot(p_list[idx][:,0], p_list[idx][:,1], marker='o', linewidth=0.3, markersize=1) + ax.plot(p_list[idx][0,0],p_list[idx][0,1],'ko',markersize=5) + if self.conf.system_id == 'car_park': + theta = p_list[idx][-1,2] + fancybox = FancyBboxPatch((0 - self.conf.L/2, 0 - self.conf.W/2), self.conf.L, self.conf.W, edgecolor='none', alpha=0.5, boxstyle='round,pad=0') + fancybox.set_transform(Affine2D().rotate_deg(np.rad2deg(theta)).translate(p_list[idx][-1,0], p_list[idx][-1,1]) + ax.transData) + ax.add_patch(fancybox) + + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + + ax.plot(self.conf.TARGET_STATE[0],self.conf.TARGET_STATE[1],'b*',markersize=10) + + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.grid(True) + fig.tight_layout() + if diff_loc==0: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationSingleInit_{}_{}'.format(self.N_try,n_updates)) + else: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationMultiInit_{}_{}'.format(self.N_try,n_updates)) + + plt.clf() + plt.close(fig) + + def rollout(self,update_step_cntr, actor_model, init_states_sim, diff_loc=0): + ''' Plot rollout of the actor from some initial states. It generates the results and then calls plot_policy() and plot_policy_eval() ''' + #tau_all_sim = [] + p_ee_all_sim = [] + + returns = {} + + for k in range(len(init_states_sim)): + rollout_controls = np.zeros((self.conf.NSTEPS,self.conf.nb_action)) + rollout_states = np.zeros((self.conf.NSTEPS+1,self.conf.nb_state)) + rollout_p_ee = np.zeros((self.conf.NSTEPS+1,3)) + rollout_episodic_reward = 0 + + rollout_p_ee[0,:] = self.env.get_end_effector_position(init_states_sim[k]) + rollout_states[0,:] = np.copy(init_states_sim[k]) + + for i in range(self.conf.NSTEPS): + rollout_controls[i,:] = tf.squeeze(self.NN.eval(actor_model, np.array([rollout_states[i,:]]))).numpy() + rollout_states[i+1,:], rwrd_sim = self.env.step(self.conf.cost_weights_running, rollout_states[i,:],rollout_controls[i,:]) + rollout_p_ee[i+1,:] = self.env.get_end_effector_position(rollout_states[i+1,:]) + + rollout_p_ee[i+1,-1] = rollout_states[i+1,2] ### !!! ### + + rollout_episodic_reward += rwrd_sim + + if k==0: + print("N try = {}: Simulation Return @ N updates = {} ==> {}".format(self.N_try,update_step_cntr,rollout_episodic_reward)) + + p_ee_all_sim.append(rollout_p_ee) + + returns[init_states_sim[k][0],init_states_sim[k][1]] = rollout_episodic_reward + + self.plot_policy_eval(p_ee_all_sim,update_step_cntr, diff_loc=diff_loc) + + return returns + + def plot_results(self, tau, ee_pos_TO, ee_pos_RL, steps, to=0): + ''' Plot results from TO and episode to check consistency ''' + timesteps = self.conf.dt*np.arange(steps+1) + fig = plt.figure(figsize=(12,8)) + if to: + plt.suptitle('TO EXPLORATION: N try = {}'.format(self.N_try), y=1, fontsize=20) + else: + plt.suptitle('POLICY EXPLORATION: N try = {}'.format(self.N_try), y=1, fontsize=20) + + ax1 = fig.add_subplot(2, 2, 1) + ax1.plot(timesteps, ee_pos_TO[:,0], 'ro', linewidth=1, markersize=1,legend="x_TO") + ax1.plot(timesteps, ee_pos_TO[:,1], 'bo', linewidth=1, markersize=1,legend="y_TO") + ax1.plot(timesteps, ee_pos_RL[:,0], 'go', linewidth=1, markersize=1,legend="x_RL") + ax1.plot(timesteps, ee_pos_RL[:,1], 'ko', linewidth=1, markersize=1,legend="y_RL") + ax1.set_xlabel('Time [s]') + ax1.set_ylabel('[m]') + ax1.set_title('End-Effector Position') + ax1.set_xlim(0, timesteps[-1]) + ax1.legend() + ax1.grid(True) + + ax2 = fig.add_subplot(2, 2, 3) + col = ['ro', 'bo', 'go'] + for i in range(self.conf.nb_action): + ax2.plot(timesteps[:-1], tau[:,i], col[i], linewidth=1, markersize=1,legend='tau{}'.format(i)) + ax2.set_xlabel('Time [s]') + ax2.set_title('Controls') + ax2.legend() + ax2.grid(True) + + ax3 = fig.add_subplot(1, 2, 2) + ax3.plot(ee_pos_TO[:,0], ee_pos_TO[:,1], 'ro', linewidth=1, markersize=2,legend='TO') + ax3.plot(ee_pos_RL[:,0], ee_pos_RL[:,1], 'bo', linewidth=1, markersize=2,legend='RL') + ax3.plot([ee_pos_TO[0,0]],[ee_pos_TO[0,1]],'ro',markersize=5) + ax3.plot([ee_pos_RL[0,0]],[ee_pos_RL[0,1]],'bo',markersize=5) + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax3.add_artist(obs_plot_list[i]) + ax3.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax3.set_xlim(self.xlim) + ax3.set_ylim(self.ylim) + ax3.set_aspect('equal', 'box') + ax3.set_xlabel('X [m]') + ax3.set_ylabel('Y [m]') + ax3.set_title('Plane') + ax3.legend() + ax3.grid(True) + + fig.tight_layout() + #plt.show() + + def plot_Return(self, ep_reward_list): + ''' Plot returns (not so meaningful given that the initial state, so also the time horizon, of each episode is randomized) ''' + fig = plt.figure(figsize=(15,8)) + ax = fig.add_subplot(1, 1, 1) + ax.set_yscale('log') + ax.plot(ep_reward_list**2) + ax.set_xlabel("Episode") + ax.set_ylabel("Return") + ax.set_title("N_try = {}".format(self.N_try)) + ax.grid(True) + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/EpReturn_{}'.format(self.N_try)) + plt.close() + + def plot_Critic_Value_function(self, critic_model, n_update, sys_id, name='V'): + ''' Plot Value function as learned by the critic ''' + if sys_id == 'manipulator': + N_discretization_x = 60 + 1 + N_discretization_y = 60 + 1 + + plot_data = np.zeros(N_discretization_y*N_discretization_x)*np.nan + ee_pos = np.zeros((N_discretization_y*N_discretization_x,3))*np.nan + + for k_x in range(N_discretization_x): + for k_y in range(N_discretization_y): + ICS = self.env.reset() + ICS[-1] = 0 + ee_pos[k_x*(N_discretization_y)+k_y,:] = self.env.get_end_effector_position(ICS) + plot_data[k_x*(N_discretization_y)+k_y] = self.NN.eval(critic_model, np.array([ICS])) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot() + plt.scatter(ee_pos[:,0], ee_pos[:,1], c=plot_data, cmap=cm.coolwarm, antialiased=False) + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + plt.colorbar() + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/{}_{}'.format(self.conf.Fig_path,self.N_try,name,int(n_update))) + plt.close() + + else: + N_discretization_x = 30 + 1 + N_discretization_y = 30 + 1 + + plot_data = np.zeros((N_discretization_y,N_discretization_x))*np.nan + + ee_x = np.linspace(-15, 15, N_discretization_x) + ee_y = np.linspace(-15, 15, N_discretization_y) + + for k_y in range(N_discretization_y): + for k_x in range(N_discretization_x): + p_ee = np.array([ee_x[k_x], ee_y[k_y], 0]) + ICS, continue_flag = self.compute_ICS(p_ee, sys_id, continue_flag=0) + if continue_flag: + continue + plot_data[k_x,k_y] = self.NN.eval(critic_model, np.array([ICS])) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot() + plt.contourf(ee_x, ee_y, plot_data.T, cmap=cm.coolwarm, antialiased=False) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + plt.colorbar() + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/{}_{}'.format(self.conf.Fig_path,self.N_try,name,int(n_update))) + plt.close() + + def plot_Critic_Value_function_from_sample(self, n_update, NSTEPS_SH, state_arr, reward_arr): + # Store transition after computing the (partial) cost-to go when using n-step TD (from 0 to Monte Carlo) + reward_to_go_arr = np.zeros(sum(NSTEPS_SH)+len(NSTEPS_SH)*1) + idx = 0 + for n in range(len(NSTEPS_SH)): + for i in range(NSTEPS_SH[n]+1): + # Compute the partial cost to go + reward_to_go_arr[idx] = sum(reward_arr[n][i:]) + idx += 1 + + state_arr = np.concatenate(state_arr, axis=0) + ee_pos_arr = np.zeros((len(state_arr),3)) + for i in range(state_arr.shape[0]): + ee_pos_arr[i,:] = self.env.get_end_effector_position(state_arr[i]) + + + mi = min(reward_to_go_arr) + ma = max(reward_to_go_arr) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot()#projection='3d') + norm = colors.Normalize(vmin=mi,vmax=ma) + + obs_plot_list = self.plot_obstaces(a=0.5) + + ax.scatter(ee_pos_arr[:,0],ee_pos_arr[:,1], c=reward_to_go_arr, norm=norm, cmap=cm.coolwarm, marker='x') + + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + + plt.colorbar(cm.ScalarMappable(norm=norm,cmap=cm.coolwarm)) + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/V_sample_{}'.format(self.conf.Fig_path,self.N_try,int(n_update))) + plt.close() + + def plot_ICS(self,state_arr): + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + for j in range(len(state_arr)): + ax.scatter(state_arr[j][0,0],state_arr[j][0,1]) + obs_plot_list = plot_fun.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.fig_ax_lim[0].tolist()) + ax.set_ylim(self.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/ICS_{}_S{}'.format(conf.Fig_path,N_try,update_step_counter,int(w_S))) + plt.close(fig) + + def plot_rollout_and_traj_from_ICS(self, init_state, n_update, actor_model, TrOp, tag, steps=200): + ''' Plot results from TO and episode to check consistency ''' + colors = cm.coolwarm(np.linspace(0.1,1,len(init_state))) + + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + + for j in range(len(init_state)): + + ee_pos_TO = np.zeros((steps,3)) + ee_pos_RL = np.zeros((steps,3)) + + RL_states = np.zeros((steps,self.conf.nb_state)) + RL_action = np.zeros((steps-1,self.conf.nb_action)) + RL_states[0,:] = init_state[j,:] + ee_pos_RL[0,:] = self.env.get_end_effector_position(RL_states[0,:]) + + for i in range(steps-1): + RL_action[i,:] = self.NN.eval(actor_model, np.array([RL_states[i,:]])) + RL_states[i+1,:] = self.env.simulate(RL_states[i,:], RL_action[i,:]) + ee_pos_RL[i+1,:] = self.env.get_end_effector_position(RL_states[i+1,:]) + + TO_states, _ = TrOp.TO_System_Solve3(init_state[j,:], RL_states.T, RL_action.T, steps-1) + + try: + for i in range(steps): + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[i,:]) + except: + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[0,:]) + + ax.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax.scatter(ee_pos_TO[0,0],ee_pos_TO[0,1],color=colors[j]) + ax.scatter(ee_pos_RL[0,0],ee_pos_RL[0,1],color=colors[j]) + ax.plot(ee_pos_TO[1:,0],ee_pos_TO[1:,1],color=colors[j]) + ax.plot(ee_pos_RL[1:,0],ee_pos_RL[1:,1],'--',color=colors[j]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.set_title('Plane') + #ax.legend() + ax.grid(True) + + plt.savefig('{}/N_try_{}/ee_traj_{}_{}'.format(self.conf.Fig_path,self.N_try,int(n_update), tag)) + + def plot_ICS(self, input_arr, cs=0): + if cs == 1: + p_arr = np.zeros((len(input_arr),3)) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + for j in range(len(input_arr)): + p_arr[j,:] = input_arr[j,:] + ax.scatter(p_arr[:,0],p_arr[:,1]) + obs_plot_list = self.plot_obstaces(a = 0.5) + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.conf.fig_ax_lim[0].tolist()) + ax.set_ylim(self.conf.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + ax.grid() + plt.savefig('{}/N_try_{}/ICS'.format(self.conf.Fig_path,self.N_try)) + plt.close(fig) + else: + p_arr = np.zeros((len(input_arr),3)) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + + for j in range(len(input_arr)): + p_arr[j,:] = self.env.get_end_effector_position(input_arr[j]) + ax.scatter(p_arr[:,0],p_arr[:,1]) + obs_plot_list = self.plot_obstaces(a = 0.5) + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.conf.fig_ax_lim[0].tolist()) + ax.set_ylim(self.conf.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + ax.grid() + plt.savefig('{}/N_try_{}/ICS'.format(self.conf.Fig_path,self.N_try)) + plt.close(fig) + + def plot_traj_from_ICS(self, init_state, TrOp, RLAC, update_step_counter=0,ep=0,steps=200, init=0,continue_flag=1): + ''' Plot results from TO and episode to check consistency ''' + colors = cm.coolwarm(np.linspace(0.1,1,len(init_state))) + + fig = plt.figure(figsize=(12,8)) + ax1 = fig.add_subplot(1,2,1) + ax2 = fig.add_subplot(1,2,2) + + for j in range(len(init_state)): + + ee_pos_TO = np.zeros((steps,3)) + ee_pos_RL = np.zeros((steps,3)) + + if init == 0: + # zeros + _, init_TO_states, init_TO_controls, _, success_init_flag = RLAC.create_TO_init(0, init_state[j,:]) + elif init == 1: + # NN + _, init_TO_states, init_TO_controls, _, success_init_flag = RLAC.create_TO_init(1, init_state[j,:]) + + if success_init_flag: + _, _, TO_states, _, _, _ = TrOp.TO_System_Solve(init_state[j,:], init_TO_states, init_TO_controls, steps-1) + else: + continue + + try: + for i in range(steps): + ee_pos_RL[i,:] = self.env.get_end_effector_position(init_TO_states[i,:]) + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[i,:]) + except: + ee_pos_RL[i,:] = self.env.get_end_effector_position(init_TO_states[0,:]) + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[0,:]) + + ax1.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax1.scatter(ee_pos_RL[0,0],ee_pos_RL[0,1],color=colors[j]) + ax1.plot(ee_pos_RL[1:,0],ee_pos_RL[1:,1],'--',color=colors[j]) + + ax2.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax2.scatter(ee_pos_TO[0,0],ee_pos_TO[0,1],color=colors[j]) + ax2.plot(ee_pos_TO[1:,0],ee_pos_TO[1:,1],color=colors[j]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax1.add_patch(obs_plot_list[i]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax2.add_patch(obs_plot_list[i]) + + ax1.set_xlim(self.xlim) + ax1.set_ylim(self.ylim) + ax1.set_aspect('equal', 'box') + ax1.set_xlabel('X [m]') + ax1.set_ylabel('Y [m]') + ax1.set_title('Warmstart traj.') + + ax2.set_xlim(self.xlim) + ax2.set_ylim(self.ylim) + ax2.set_aspect('equal', 'box') + ax2.set_xlabel('X [m]') + #ax2.set_ylabel('Y [m]') + ax2.set_title('TO traj.') + #ax.legend() + ax1.grid(True) + ax2.grid(True) + + plt.savefig('{}/N_try_{}/ee_traj_{}_{}'.format(self.conf.Fig_path,self.N_try,init,update_step_counter)) + + + + +if __name__ == '__main__': + import os + import sys + import time + import random + import importlib + import numpy as np + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # {'0' -> show all logs, '1' -> filter out info, '2' -> filter out warnings} + import tensorflow as tf + import matplotlib.pyplot as plt + import mpl_toolkits.mplot3d.art3d as art3d + + from RL import RL_AC + from plot_utils import PLOT + from NeuralNetwork import NN + + ### Input ### + N_try = 0 + + seed = 0 + tf.random.set_seed(seed) # Set tensorflow seed + random.seed(seed) # Set random seed + + system_id = 'car_park' + + TO_method = 'casadi' + + recover_training_flag = 0 + + CPU_flag = 0 + if CPU_flag: + os.environ["CUDA_VISIBLE_DEVICES"]="-1" + tf.config.experimental.list_physical_devices('GPU') + + nb_cpus = 1 + + w_S = 0 + ################################# + + # Import configuration file and environment file + system_map = { + 'single_integrator': ('conf_single_integrator', 'SingleIntegrator'), + 'double_integrator': ('conf_double_integrator', 'DoubleIntegrator'), + 'car': ('conf_car', 'Car'), + 'car_park': ('conf_car_park', 'CarPark'), + 'manipulator': ('conf_manipulator', 'Manipulator'), + 'ur5': ('conf_ur5', 'UR5') + } + + try: + conf_module, env_class = system_map[system_id] + conf = importlib.import_module(conf_module) + Environment = getattr(importlib.import_module('environment'), env_class) + except KeyError: + print('System {} not found'.format(system_id)) + sys.exit() + + + + # Create folders to store the results and the trained NNs and save configuration + for path in conf.path_list: + os.makedirs(path + '/N_try_{}'.format(N_try), exist_ok=True) + os.makedirs(conf.Config_path, exist_ok=True) + + params = [p for p in conf.__dict__ if not p.startswith("__")] + with open(conf.Config_path + '/config{}.txt'.format(N_try), 'w') as f: + for p in params: + f.write('{} = {}\n'.format(p, conf.__dict__[p])) + f.write('Seed = {}\n'.format(seed)) + f.write('w_S = {}'.format(w_S)) + + + + # Create environment instances + env = Environment(conf) + + # Create NN instance + NN_inst = NN(env, conf, w_S) + + # Create RL_AC instance + RLAC = RL_AC(env, NN_inst, conf, N_try) + + # Set initial weights of the NNs, initialize the counter of the updates and setup NN models + if recover_training_flag: + recover_training = np.array([conf.NNs_path_rec, conf.N_try_rec, conf.update_step_counter_rec]) + update_step_counter = conf.update_step_counter_rec + nb_starting_episode = (conf.update_step_counter_rec/conf.UPDATE_LOOPS)+1 + + RLAC.setup_model(recover_training) + else: + update_step_counter = 0 + nb_starting_episode = 0 + + RLAC.setup_model() + + # Create PLOT instance + plot_fun = PLOT(N_try, env, NN_inst, conf) + + plot_fun.plot_Reward() \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_100/replay_buffer.py b/Results Single Integrator/Results set test/Code/N_try_100/replay_buffer.py new file mode 100644 index 0000000..ba1804d --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/replay_buffer.py @@ -0,0 +1,240 @@ +import math +import random +import numpy as np +import tensorflow as tf +import segment_tree +#from stable_baselines.common.segment_tree import SumSegmentTree, MinSegmentTree + + +class ReplayBuffer(object): + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param BATCH_SIZE : (int) Size of the mini-batch + :param nb_state : (int) State size (robot state size + 1) + ''' + + self.conf = conf + self.storage_mat = np.zeros((conf.REPLAY_SIZE, conf.nb_state + 1 + conf.nb_state + conf.nb_state + 1 + 1)) + self.next_idx = 0 + self.full = 0 + self.exp_counter = np.zeros(conf.REPLAY_SIZE) + + def add(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Add transitions to the buffer ''' + data = self.concatenate_sample(obses_t, rewards, obses_t1, dVdxs, dones, terms) + + if len(data) + self.next_idx > self.conf.REPLAY_SIZE: + self.storage_mat[self.next_idx:,:] = data[:self.conf.REPLAY_SIZE-self.next_idx,:] + self.storage_mat[:self.next_idx+len(data)-self.conf.REPLAY_SIZE,:] = data[self.conf.REPLAY_SIZE-self.next_idx:,:] + self.full = 1 + else: + self.storage_mat[self.next_idx:self.next_idx+len(data),:] = data + + self.next_idx = (self.next_idx + len(data)) % self.conf.REPLAY_SIZE + + def sample(self): + ''' Sample a batch of transitions ''' + # Select indexes of the batch elements + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + idxes = np.random.randint(0, max_idx, size=self.conf.BATCH_SIZE) + + obses_t = self.storage_mat[idxes, :self.conf.nb_state] + rewards = self.storage_mat[idxes, self.conf.nb_state:self.conf.nb_state+1] + obses_t1 = self.storage_mat[idxes, self.conf.nb_state+1:self.conf.nb_state*2+1] + dVdxs = self.storage_mat[idxes, self.conf.nb_state*2+1:self.conf.nb_state*3+1] + dones = self.storage_mat[idxes, self.conf.nb_state*3+1:self.conf.nb_state*3+2] + terms = self.storage_mat[idxes, self.conf.nb_state*3+2:self.conf.nb_state*3+3] + + # Priorities not used + weights = np.ones((self.conf.BATCH_SIZE,1)) + batch_idxes = None + + # Convert the sample in tensor + obses_t, rewards, obses_t1, dVdxs, dones, weights = self.convert_sample_to_tensor(obses_t, rewards, obses_t1, dVdxs, dones, weights) + + return obses_t, rewards, obses_t1, dVdxs, dones, terms, weights, batch_idxes + + def concatenate_sample(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Convert batch of transitions into a tensor ''' + obses_t = np.concatenate(obses_t, axis=0) + rewards = np.concatenate(rewards, axis=0) + obses_t1 = np.concatenate(obses_t1, axis=0) + dVdxs = np.concatenate(dVdxs, axis=0) + dones = np.concatenate(dones, axis=0) + terms = np.concatenate(terms, axis=0) + + return np.concatenate((obses_t, rewards.reshape(-1,1), obses_t1, dVdxs, dones.reshape(-1,1), terms.reshape(-1,1)),axis=1) + + def convert_sample_to_tensor(self, obses_t, rewards, obses_t1, dVdxs, dones, weights): + ''' Convert batch of transitions into a tensor ''' + obses_t = tf.convert_to_tensor(obses_t, dtype=tf.float32) + rewards = tf.convert_to_tensor(rewards, dtype=tf.float32) + obses_t1 = tf.convert_to_tensor(obses_t1, dtype=tf.float32) + dVdxs = tf.convert_to_tensor(dVdxs, dtype=tf.float32) + dones = tf.convert_to_tensor(dones, dtype=tf.float32) + weights = tf.convert_to_tensor(weights, dtype=tf.float32) + + return obses_t, rewards, obses_t1, dVdxs, dones, weights + + + +class PrioritizedReplayBuffer: + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param BATCH_SIZE : (int) Size of the mini-batch + :param nb_state : (int) State size (robot state size + 1) + :param prioritized_replay_alpha : (float) Determines how much prioritization is used, set to 0 to use a normal buffer + :param prioritized_replay_beta : (float) Small positive constant that prevents the edge-case of transitions not being revisited once their error is zero + ''' + + self.conf = conf + + self.storage_mat = np.zeros((conf.REPLAY_SIZE, conf.nb_state + 1 + conf.nb_state + conf.nb_state + 1 + 1)) + self.full = 0 + self.next_idx = 0 + self.exp_counter = np.zeros(self.conf.REPLAY_SIZE) + self.priorities = np.empty(self.conf.REPLAY_SIZE) + + assert conf.prioritized_replay_alpha >= 0 + assert conf.prioritized_replay_beta > 0 + + it_capacity = 1 + while it_capacity < self.conf.REPLAY_SIZE: + it_capacity *= 2 + + self._it_sum = SumSegmentTree(it_capacity) + self._it_min = MinSegmentTree(it_capacity) + self._max_priority = 1.0 + + #self.RB_type = 'ReLO' + + self.MSE = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE) + + def add(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Add transitions to the buffer ''' + data = self.concatenate_sample(obses_t, rewards, obses_t1, dVdxs, dones, terms) + + if len(data) + self.next_idx > self.conf.REPLAY_SIZE: + self.storage_mat[self.next_idx:,:] = data[:self.conf.REPLAY_SIZE-self.next_idx,:] + self.storage_mat[:self.next_idx+len(data)-self.conf.REPLAY_SIZE,:] = data[self.conf.REPLAY_SIZE-self.next_idx:,:] + self.full = 1 + else: + self.storage_mat[self.next_idx:self.next_idx+len(data),:] = data + + for i in range(len(data)): + self._it_sum[(self.next_idx+i) % self.conf.REPLAY_SIZE] = self._max_priority ** self.conf.prioritized_replay_alpha + self._it_min[(self.next_idx+i) % self.conf.REPLAY_SIZE] = self._max_priority ** self.conf.prioritized_replay_alpha + + self.next_idx = (self.next_idx + len(data)) % self.conf.REPLAY_SIZE + + def _sample_proportional(self): + ''' Sample a batch of transitions ''' + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + + idx_arr = np.zeros(self.conf.BATCH_SIZE) + + p_total = self._it_sum.sum(0, max_idx - 1) + + segment = p_total / self.conf.BATCH_SIZE + + for i in range(self.conf.BATCH_SIZE): + p = random.random() * segment + i * segment #random.random(segment*i, segment*(i+1)) + idx = self._it_sum.find_prefixsum_idx(p) + idx_arr[i] = idx + + return idx_arr + + def sample(self): + ''' Sample a batch of transitions ''' + # Select indexes of the batch elements + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + + idxes = self._sample_proportional() + batch_idxes = idxes.astype(int) + + # Compute weights normalization + p_min = self._it_min.min() / self._it_sum.sum() + max_weight = (p_min * max_idx) ** (-self.conf.prioritized_replay_beta) + + self.exp_counter[batch_idxes] += 1 + self.priorities[batch_idxes] = self._it_sum[batch_idxes] / self._it_sum.sum() + weights = (self.priorities[batch_idxes] * max_idx) ** (-self.conf.prioritized_replay_beta) / max_weight + + obses_t = self.storage_mat[batch_idxes, :self.conf.nb_state] + rewards = self.storage_mat[batch_idxes, self.conf.nb_state:self.conf.nb_state+1] + obses_t1 = self.storage_mat[batch_idxes, self.conf.nb_state+1:self.conf.nb_state*2+1] + dVdxs = self.storage_mat[batch_idxes, self.conf.nb_state*2+1:self.conf.nb_state*3+1] + dones = self.storage_mat[batch_idxes, self.conf.nb_state*3+1:self.conf.nb_state*3+2] + terms = self.storage_mat[batch_idxes, self.conf.nb_state*3+2:self.conf.nb_state*3+3] + + # Convert the sample in tensor + obses_t, rewards, obses_t1, dVdxs, dones, weights = self.convert_sample_to_tensor(obses_t, rewards, obses_t1, dVdxs, dones, weights) + + return obses_t, rewards, obses_t1, dVdxs, dones, terms, weights, batch_idxes + + def update_priorities(self, idxes, reward_to_go_batch, critic_value, target_critic_value=None): + '''Update priorities of sampled transitions ''' + # Create td_errors + if self.RB_type == 'ReLO': + # MSE(Delta V_c) - MSE(Delta V_t) + td_errors = self.MSE(reward_to_go_batch, critic_value).numpy()-self.MSE(reward_to_go_batch, target_critic_value).numpy() + td_errors_norm = np.clip(td_errors, 0, np.max(td_errors)) + else: # 'PER' is default self.RB_type + # |TD_error_i| + td_errors_norm = tf.math.abs(tf.math.subtract(reward_to_go_batch, critic_value)) + td_errors_norm = td_errors_norm[:,0] + + # Compute the freshness discount factor + fresh_disc_factor = self.conf.fresh_factor**self.exp_counter[idxes] + + # Compute new priorities: p_i = mu**C_i * td_error + self.conf.prioritized_replay_eps + new_priorities = fresh_disc_factor * td_errors_norm + self.conf.prioritized_replay_eps + + # Sets priority of transition at index idxes[i] in buffer to priorities[i] + assert len(idxes) == len(new_priorities) + for idx, priority in zip(idxes, new_priorities): + assert priority > 0 + + idx = int(idx) + + self._it_sum[idx] = priority ** self.conf.prioritized_replay_alpha + self._it_min[idx] = priority ** self.conf.prioritized_replay_alpha + + self._max_priority = max(self._max_priority, priority) + + def concatenate_sample(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Convert batch of transitions into a tensor ''' + obses_t = np.concatenate(obses_t, axis=0) + rewards = np.concatenate(rewards, axis=0) + obses_t1 = np.concatenate(obses_t1, axis=0) + dVdxs = np.concatenate(dVdxs, axis=0) + dones = np.concatenate(dones, axis=0) + terms = np.concatenate(terms, axis=0) + + return np.concatenate((obses_t, rewards.reshape(-1,1), obses_t1, dVdxs, dones.reshape(-1,1), terms.reshape(-1,1)),axis=1) + + def convert_sample_to_tensor(self, obses_t, rewards, obses_t1, dVdxs, dones, weights): + ''' Convert batch of transitions into a tensor ''' + obses_t = tf.convert_to_tensor(obses_t, dtype=tf.float32) + rewards = tf.convert_to_tensor(rewards, dtype=tf.float32) + obses_t1 = tf.convert_to_tensor(obses_t1, dtype=tf.float32) + dVdxs = tf.convert_to_tensor(dVdxs, dtype=tf.float32) + dones = tf.convert_to_tensor(dones, dtype=tf.float32) + weights = tf.convert_to_tensor(weights, dtype=tf.float32) + + return obses_t, rewards, obses_t1, dVdxs, dones, weights diff --git a/Results Single Integrator/Results set test/Code/N_try_100/robot_utils.py b/Results Single Integrator/Results set test/Code/N_try_100/robot_utils.py new file mode 100644 index 0000000..815ff31 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/robot_utils.py @@ -0,0 +1,637 @@ +import os +import math +import random +import gepetto#.corbaserver +import time +import subprocess +import numpy as np +import pinocchio as pin +from numpy.linalg import norm +from pinocchio.robot_wrapper import RobotWrapper as PinocchioRobotWrapper + +class Contact: + ''' A contact between a contact-point and a contact-surface + ''' + def __init__(self, contact_point, contact_surface): + self.cp = contact_point + self.cs = contact_surface + self.reset_contact_position() + + def reset_contact_position(self): + # Initialize anchor point p0, that is the initial (0-load) position of the spring + self.p0 = self.cp.get_position() + self.in_contact = True + + def compute_force(self): + self.f, self.p0 = self.cs.compute_force(self.cp, self.p0) + return self.f + + def get_jacobian(self): + return self.cp.get_jacobian() + +class RobotWrapper(PinocchioRobotWrapper): + + @staticmethod + def BuildFromURDF(filename, package_dirs=None, root_joint=None, verbose=False, meshLoader=None): + robot = RobotWrapper() + robot.initFromURDF(filename, package_dirs, root_joint, verbose, meshLoader) + return robot + + @property + def na(self): + if(self.model.joints[0].nq==7): + return self.model.nv-6 + return self.model.nv + + def mass(self, q, update=True): + if(update): + return pin.crba(self.model, self.data, q) + return self.data.M + + def nle(self, q, v, update=True): + if(update): + return pin.nonLinearEffects(self.model, self.data, q, v) + return self.data.nle + + def com(self, q=None, v=None, a=None, update=True): + if(update==False or q is None): + return PinocchioRobotWrapper.com(self, q) + if a is None: + if v is None: + return PinocchioRobotWrapper.com(self, q) + return PinocchioRobotWrapper.com(self, q, v) + return PinocchioRobotWrapper.com(self, q, v,a) + + def Jcom(self, q, update=True): + if(update): + return pin.jacobianCenterOfMass(self.model, self.data, q) + return self.data.Jcom + + def momentumJacobian(self, q, v, update=True): + if(update): + pin.ccrba(self.model, self.data, q, v) + return self.data.Ag + + def computeAllTerms(self, q, v): + ''' pin.computeAllTerms is equivalent to calling: + pinocchio::forwardKinematics + pinocchio::crba + pinocchio::nonLinearEffects + pinocchio::computeJointJacobians + pinocchio::centerOfMass + pinocchio::jacobianCenterOfMass + pinocchio::kineticEnergy + pinocchio::potentialEnergy + This is too much for our needs, so we call only the functions + we need, including those for the frame kinematics + ''' + #pin.computeAllTerms(self.model, self.data, q, v); + pin.forwardKinematics(self.model, self.data, q, v, np.zeros(self.model.nv)) + pin.computeJointJacobians(self.model, self.data) + pin.updateFramePlacements(self.model, self.data) + pin.crba(self.model, self.data, q) + pin.nonLinearEffects(self.model, self.data, q, v) + + def forwardKinematics(self, q, v=None, a=None): + if v is not None: + if a is not None: + pin.forwardKinematics(self.model, self.data, q, v, a) + else: + pin.forwardKinematics(self.model, self.data, q, v) + else: + pin.forwardKinematics(self.model, self.data, q) + + def inverseKinematics(self, ee_coords): + ''' Inverse kinematics algorithm to compute a joint configuration given the EE coordinates''' + + oMdes = pin.SE3(np.eye(3), np.array([ee_coords[0], ee_coords[1], 0.0])) + random.seed(123) + for j in range(100): + q = np.array([random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi)]) + # q = pin.neutral(self.model) + + pin.forwardKinematics(self.model, self.data, q) + pin.updateFramePlacements(self.model, self.data) + eps = 1e-4 + IT_MAX = 1000 + DT = 1e-1 + damp = 1e-12 + frame_id = self.model.getFrameId('fixed_ee') + i=0 + while True: + pin.forwardKinematics(self.model,self.data,q) + pin.updateFramePlacements(self.model, self.data) + dMi = oMdes.actInv(self.data.oMf[frame_id]) + err = pin.log(dMi).vector + if norm(err) < eps: + success = True + break + if i >= IT_MAX: + success = False + break + J = pin.computeFrameJacobian(self.model,self.data,q,frame_id) + v = - J.T.dot(np.linalg.solve(J.dot(J.T) + damp * np.eye(6), err)) + q = pin.integrate(self.model,q,v*DT) + if not i % 10: + print('%d: error = %s' % (i, err.T)) + i += 1 + + if success: + print("Convergence achieved!") + break + else: + print("\nWarning: the iterative algorithm has not reached convergence to the desired precision. Retry ({}) with another initial configuration".format(j)) + + return q, success + + def frameJacobian(self, q, index, update=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + ''' Call computeFrameJacobian if update is true. If not, user should call computeFrameJacobian first. + Then call getFrameJacobian and return the Jacobian matrix. + ref_frame can be: ReferenceFrame.LOCAL, ReferenceFrame.WORLD, ReferenceFrame.LOCAL_WORLD_ALIGNED + ''' + if(update): + pin.computeFrameJacobian(self.model, self.data, q, index) + return pin.getFrameJacobian(self.model, self.data, index, ref_frame) + + def frameVelocity(self, q, v, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v) + v_local = pin.getFrameVelocity(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return v_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + v_world = H.act(v_local) + return v_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + v = Hr.act(v_local) + return v + + def frameAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def frameClassicAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + v = pin.getFrameVelocity(self.model, self.data, index) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + a_local.linear += np.cross(v.angular, v.linear, axis=0) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def deactivateCollisionPairs(self, collision_pair_indexes): + for i in collision_pair_indexes: + self.collision_data.deactivateCollisionPair(i) + + def addAllCollisionPairs(self): + self.collision_model.addAllCollisionPairs() + self.collision_data = pin.GeometryData(self.collision_model) + + def isInCollision(self, q, stop_at_first_collision=True): + return pin.computeCollisions(self.model, self.data, self.collision_model, self.collision_data, np.asmatrix(q).reshape((self.model.nq,1)), stop_at_first_collision) + + def findFirstCollisionPair(self, consider_only_active_collision_pairs=True): + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + return (i, self.collision_model.collisionPairs[i]) + return None + + def findAllCollisionPairs(self, consider_only_active_collision_pairs=True): + res = [] + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + res += [(i, self.collision_model.collisionPairs[i])] + return res + +class RobotSimulator: + + def __init__(self, robot, q0_init, v0_init, simulation_type, tau_coulomb_max, use_viewer=False, DISPLAY_T=None, CAMERA_TRANSFORM=None, show_floor=False): + self.robot = robot + self.model = self.robot.model + self.data = self.model.createData() + self.t = 0.0 # time + self.nv = nv = self.model.nv # Dimension of joint velocities vector + self.na = na = robot.na # number of actuated joints + self.S = np.hstack((np.zeros((na, nv-na)), np.eye(na, na))) # Matrix S used as filter of vector of inputs U + + self.contacts = [] + self.candidate_contact_points = [] # candidate contact points + self.contact_surfaces = [] + + self.DISPLAY_T = DISPLAY_T # refresh period for viewer + self.use_viewer = use_viewer + self.tau_coulomb_max = tau_coulomb_max + self.display_counter = self.DISPLAY_T + self.init(q0_init, v0_init, True) + + self.tau_c = np.zeros(na) # Coulomb friction torque + self.simulation_type = simulation_type + self.set_coulomb_friction(tau_coulomb_max) + + # for gepetto viewer + self.gui = None + if(use_viewer): + try: + prompt = subprocess.getstatusoutput("ps aux |grep 'gepetto-gui'|grep -v 'grep'|wc -l") + if int(prompt[1]) == 0: + os.system('gepetto-gui &') + time.sleep(1) + except: + pass + gepetto.corbaserver.Client() + gepetto.Client() + self.robot.initViewer(loadModel=True) + self.gui = self.robot.viewer.gui + if(show_floor): + self.robot.viewer.gui.createSceneWithFloor('world') + self.gui.setLightingMode('world/floor', 'OFF') + self.robot.displayCollisions(False) + self.robot.displayVisuals(True) + self.robot.display(self.q) + try: + self.gui.setCameraTransform("python-pinocchio", CAMERA_TRANSFORM) + except: + self.gui.setCameraTransform(0, CAMERA_TRANSFORM) + + # Re-initialize the simulator + def init(self, q0=None, v0=None, reset_contact_positions=False): + self.first_iter = True + + if q0 is not None: + self.q = q0.copy() + + if(v0 is None): + self.v = np.zeros(self.robot.nv) + else: + self.v = v0.copy() + self.dv = np.zeros(self.robot.nv) + self.resize_contact_data(reset_contact_positions) + + def resize_contact_data(self, reset_contact_positions=False): + self.nc = len(self.contacts) # number of contacts + self.nk = 3*self.nc # size of contact force vector + self.f = np.zeros(self.nk) # contact forces + self.Jc = np.zeros((self.nk, self.model.nv)) # contact Jacobian + + # reset contact position + if(reset_contact_positions): + pin.forwardKinematics(self.model, self.data, self.q) + pin.updateFramePlacements(self.model, self.data) + for c in self.contacts: + c.reset_contact_position() + + self.compute_forces(compute_data=True) + + def set_coulomb_friction(self, tau_max): + self.tau_coulomb_max = 1e-2*tau_max*self.model.effortLimit + self.simulate_coulomb_friction = (norm(self.tau_coulomb_max)!=0.0) + + def collision_detection(self): + for s in self.contact_surfaces: # for each contact surface + for cp in self.candidate_contact_points: # for each candidate contact point + p = cp.get_position() + if(s.check_collision(p)): # check whether the point is colliding with the surface + if(not cp.active): # if the contact was not already active + print("Collision detected between point", cp.frame_name, " at ", p) + cp.active = True + cp.contact = Contact(cp, s) + self.contacts += [cp.contact] + self.resize_contact_data() + else: + if(cp.active): + print("Contact lost between point", cp.frame_name, " at ", p) + cp.active = False + self.contacts.remove(cp.contact) + self.resize_contact_data() + + def compute_forces(self, compute_data=True): + '''Compute the contact forces from q, v and elastic model''' + if compute_data: + pin.forwardKinematics(self.model, self.data, self.q, self.v) + # Computes the placements of all the operational frames according to the current joint placement stored in data + pin.updateFramePlacements(self.model, self.data) + self.collision_detection() + + i = 0 + for c in self.contacts: + self.f[i:i+3] = c.compute_force() + self.Jc[i:i+3, :] = c.get_jacobian() + i += 3 + return self.f + + def step(self, u, dt=None): + if dt is None: + dt = self.dt + + # (Forces are directly in the world frame, and aba wants them in the end effector frame) + pin.computeAllTerms(self.model, self.data, self.q, self.v) + pin.updateFramePlacements(self.model, self.data) + M = self.data.M + h = self.data.nle + self.collision_detection() + self.compute_forces(False) + + if(self.simulate_coulomb_friction and self.simulation_type=='timestepping'): + # minimize kinetic energy using time stepping + from quadprog import solve_qp + ''' + Solve a strictly convex quadratic program + + Minimize 1/2 x^T G x - a^T x + Subject to C.T x >= b + + Input Parameters + ---------- + G : array, shape=(n, n) + a : array, shape=(n,) + C : array, shape=(n, m) matrix defining the constraints + b : array, shape=(m), default=None, vector defining the constraints + meq : int, default=0 + the first meq constraints are treated as equality constraints, + all further as inequality constraints + ''' + # M (v' - v) = dt*S^T*(tau - tau_c) - dt*h + dt*J^T*f + # M v' = M*v + dt*(S^T*tau - h + J^T*f) - dt*S^T*tau_c + # M v' = b + B*tau_c + # v' = Minv*(b + B*tau_c) + b = M.dot(self.v) + dt*(self.S.T.dot(u) - h + self.Jc.T.dot(self.f)) + B = - dt*self.S.T + # Minimize kinetic energy: + # min v'.T * M * v' + # min (b+B*tau_c​).T*Minv*(b+B*tau_c​) + # min tau_c.T * B.T*Minv*B* tau_C + 2*b.T*Minv*B*tau_c + Minv = np.linalg.inv(M) + G = B.T.dot(Minv.dot(B)) + a = -b.T.dot(Minv.dot(B)) + C = np.vstack((np.eye(self.na), -np.eye(self.na))) + c = np.concatenate((-self.tau_coulomb_max, -self.tau_coulomb_max)) + solution = solve_qp(G, a, C.T, c, 0) + self.tau_c = solution[0] + self.v = Minv.dot(b + B.dot(self.tau_c)) + self.q = pin.integrate(self.model, self.q, self.v*dt) + self.dv = np.linalg.solve(M, self.S.T.dot(u-self.tau_c) - h + self.Jc.T.dot(self.f)) + elif(self.simulation_type=='euler' or self.simulate_coulomb_friction==False): + self.tau_c = self.tau_coulomb_max*np.sign(self.v[-self.na:]) + self.dv = np.linalg.solve(M, self.S.T.dot(u-self.tau_c) - h + self.Jc.T.dot(self.f)) + # v_mean = np.copy(self.v) + 0.5*dt*self.dv + self.q = pin.integrate(self.model, self.q, self.v*dt) + # self.q += dt*v_mean + self.v += self.dv*dt + else: + print("[ERROR] Unknown simulation type:", self.simulation_type) + + self.t += dt + return self.q, self.v, self.dv + + def reset(self): + self.first_iter = True + + def simulate(self, state, u, dt=0.05, ndt=1): + ''' Perform ndt steps, each lasting dt/ndt seconds ''' + self.q = state[:self.robot.nq] + self.v = state[self.robot.nq:self.robot.nv+self.robot.nq] + + tau_c_avg = 0*self.tau_c + for i in range(ndt): + self.q, self.v, self.dv = self.step(u, dt/ndt) + tau_c_avg += self.tau_c + self.tau_c = tau_c_avg / ndt + + if (self.use_viewer): + self.display_counter -= dt + if self.display_counter <= 0.0: + self.robot.display(self.q) + self.display_counter = self.DISPLAY_T + + return self.q, self.v, self.f + + def display(self, q): + if(self.use_viewer): + self.robot.display(q) + + +class RobotWrapper(PinocchioRobotWrapper): + + @staticmethod + def BuildFromURDF(filename, package_dirs=None, root_joint=None, verbose=False, meshLoader=None): + robot = RobotWrapper() + robot.initFromURDF(filename, package_dirs, root_joint, verbose, meshLoader) + return robot + + @property + def na(self): + if(self.model.joints[0].nq==7): + return self.model.nv-6 + return self.model.nv + + def mass(self, q, update=True): + if(update): + return pin.crba(self.model, self.data, q) + return self.data.M + + def nle(self, q, v, update=True): + if(update): + return pin.nonLinearEffects(self.model, self.data, q, v) + return self.data.nle + + def com(self, q=None, v=None, a=None, update=True): + if(update==False or q is None): + return PinocchioRobotWrapper.com(self, q) + if a is None: + if v is None: + return PinocchioRobotWrapper.com(self, q) + return PinocchioRobotWrapper.com(self, q, v) + return PinocchioRobotWrapper.com(self, q, v,a) + + def Jcom(self, q, update=True): + if(update): + return pin.jacobianCenterOfMass(self.model, self.data, q) + return self.data.Jcom + + def momentumJacobian(self, q, v, update=True): + if(update): + pin.ccrba(self.model, self.data, q, v) + return self.data.Ag + + def computeAllTerms(self, q, v): + ''' pin.computeAllTerms is equivalent to calling: + pinocchio::forwardKinematics + pinocchio::crba + pinocchio::nonLinearEffects + pinocchio::computeJointJacobians + pinocchio::centerOfMass + pinocchio::jacobianCenterOfMass + pinocchio::kineticEnergy + pinocchio::potentialEnergy + This is too much for our needs, so we call only the functions + we need, including those for the frame kinematics + ''' + #pin.computeAllTerms(self.model, self.data, q, v); + pin.forwardKinematics(self.model, self.data, q, v, np.zeros(self.model.nv)) + pin.computeJointJacobians(self.model, self.data) + pin.updateFramePlacements(self.model, self.data) + pin.crba(self.model, self.data, q) + pin.nonLinearEffects(self.model, self.data, q, v) + + def forwardKinematics(self, q, v=None, a=None): + if v is not None: + if a is not None: + pin.forwardKinematics(self.model, self.data, q, v, a) + else: + pin.forwardKinematics(self.model, self.data, q, v) + else: + pin.forwardKinematics(self.model, self.data, q) + + def inverseKinematics(self, ee_coords): + ''' Inverse kinematics algorithm to compute a joint configuration given the EE coordinates''' + + oMdes = pin.SE3(np.eye(3), np.array([ee_coords[0], ee_coords[1], 0.0])) + random.seed(123) + for j in range(100): + q = np.array([random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi)]) + # q = pin.neutral(self.model) + + pin.forwardKinematics(self.model, self.data, q) + pin.updateFramePlacements(self.model, self.data) + eps = 1e-4 + IT_MAX = 1000 + DT = 1e-1 + damp = 1e-12 + frame_id = self.model.getFrameId('fixed_ee') + i=0 + while True: + pin.forwardKinematics(self.model,self.data,q) + pin.updateFramePlacements(self.model, self.data) + dMi = oMdes.actInv(self.data.oMf[frame_id]) + err = pin.log(dMi).vector + if norm(err) < eps: + success = True + break + if i >= IT_MAX: + success = False + break + J = pin.computeFrameJacobian(self.model,self.data,q,frame_id) + v = - J.T.dot(np.linalg.solve(J.dot(J.T) + damp * np.eye(6), err)) + q = pin.integrate(self.model,q,v*DT) + if not i % 10: + print('%d: error = %s' % (i, err.T)) + i += 1 + + if success: + print("Convergence achieved!") + break + else: + print("\nWarning: the iterative algorithm has not reached convergence to the desired precision. Retry ({}) with another initial configuration".format(j)) + + return q, success + + def frameJacobian(self, q, index, update=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + ''' Call computeFrameJacobian if update is true. If not, user should call computeFrameJacobian first. + Then call getFrameJacobian and return the Jacobian matrix. + ref_frame can be: ReferenceFrame.LOCAL, ReferenceFrame.WORLD, ReferenceFrame.LOCAL_WORLD_ALIGNED + ''' + if(update): + pin.computeFrameJacobian(self.model, self.data, q, index) + return pin.getFrameJacobian(self.model, self.data, index, ref_frame) + + def frameVelocity(self, q, v, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v) + v_local = pin.getFrameVelocity(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return v_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + v_world = H.act(v_local) + return v_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + v = Hr.act(v_local) + return v + + def frameAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def frameClassicAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + v = pin.getFrameVelocity(self.model, self.data, index) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + a_local.linear += np.cross(v.angular, v.linear, axis=0) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def deactivateCollisionPairs(self, collision_pair_indexes): + for i in collision_pair_indexes: + self.collision_data.deactivateCollisionPair(i) + + def addAllCollisionPairs(self): + self.collision_model.addAllCollisionPairs() + self.collision_data = pin.GeometryData(self.collision_model) + + def isInCollision(self, q, stop_at_first_collision=True): + return pin.computeCollisions(self.model, self.data, self.collision_model, self.collision_data, np.asmatrix(q).reshape((self.model.nq,1)), stop_at_first_collision) + + def findFirstCollisionPair(self, consider_only_active_collision_pairs=True): + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + return (i, self.collision_model.collisionPairs[i]) + return None + + def findAllCollisionPairs(self, consider_only_active_collision_pairs=True): + res = [] + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + res += [(i, self.collision_model.collisionPairs[i])] + return res \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_100/segment_tree.py b/Results Single Integrator/Results set test/Code/N_try_100/segment_tree.py new file mode 100644 index 0000000..cb386ec --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/segment_tree.py @@ -0,0 +1,145 @@ +import operator + + +class SegmentTree(object): + def __init__(self, capacity, operation, neutral_element): + """Build a Segment Tree data structure. + + https://en.wikipedia.org/wiki/Segment_tree + + Can be used as regular array, but with two + important differences: + + a) setting item's value is slightly slower. + It is O(lg capacity) instead of O(1). + b) user has access to an efficient ( O(log segment size) ) + `reduce` operation which reduces `operation` over + a contiguous subsequence of items in the array. + + Paramters + --------- + capacity: int + Total size of the array - must be a power of two. + operation: lambda obj, obj -> obj + and operation for combining elements (eg. sum, max) + must form a mathematical group together with the set of + possible values for array elements (i.e. be associative) + neutral_element: obj + neutral element for the operation above. eg. float('-inf') + for max and 0 for sum. + """ + assert capacity > 0 and capacity & (capacity - 1) == 0, "capacity must be positive and a power of 2." + self._capacity = capacity + self._value = [neutral_element for _ in range(2 * capacity)] + self._operation = operation + + def _reduce_helper(self, start, end, node, node_start, node_end): + if start == node_start and end == node_end: + return self._value[node] + mid = (node_start + node_end) // 2 + if end <= mid: + return self._reduce_helper(start, end, 2 * node, node_start, mid) + else: + if mid + 1 <= start: + return self._reduce_helper(start, end, 2 * node + 1, mid + 1, node_end) + else: + return self._operation( + self._reduce_helper(start, mid, 2 * node, node_start, mid), + self._reduce_helper(mid + 1, end, 2 * node + 1, mid + 1, node_end) + ) + + def reduce(self, start=0, end=None): + """Returns result of applying `self.operation` + to a contiguous subsequence of the array. + + self.operation(arr[start], operation(arr[start+1], operation(... arr[end]))) + + Parameters + ---------- + start: int + beginning of the subsequence + end: int + end of the subsequences + + Returns + ------- + reduced: obj + result of reducing self.operation over the specified range of array elements. + """ + if end is None: + end = self._capacity + if end < 0: + end += self._capacity + end -= 1 + return self._reduce_helper(start, end, 1, 0, self._capacity - 1) + + def __setitem__(self, idx, val): + # index of the leaf + idx += self._capacity + self._value[idx] = val + idx //= 2 + while idx >= 1: + self._value[idx] = self._operation( + self._value[2 * idx], + self._value[2 * idx + 1] + ) + idx //= 2 + + def __getitem__(self, idx): + assert 0 <= idx < self._capacity + return self._value[self._capacity + idx] + + +class SumSegmentTree(SegmentTree): + def __init__(self, capacity): + super(SumSegmentTree, self).__init__( + capacity=capacity, + operation=operator.add, + neutral_element=0.0 + ) + + def sum(self, start=0, end=None): + """Returns arr[start] + ... + arr[end]""" + return super(SumSegmentTree, self).reduce(start, end) + + def find_prefixsum_idx(self, prefixsum): + """Find the highest index `i` in the array such that + sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum + + if array values are probabilities, this function + allows to sample indexes according to the discrete + probability efficiently. + + Parameters + ---------- + perfixsum: float + upperbound on the sum of array prefix + + Returns + ------- + idx: int + highest index satisfying the prefixsum constraint + """ + assert 0 <= prefixsum <= self.sum() + 1e-5 + idx = 1 + while idx < self._capacity: # while non-leaf + if self._value[2 * idx] > prefixsum: + idx = 2 * idx + else: + prefixsum -= self._value[2 * idx] + idx = 2 * idx + 1 + return idx - self._capacity + + +class MinSegmentTree(SegmentTree): + def __init__(self, capacity): + super(MinSegmentTree, self).__init__( + capacity=capacity, + operation=min, + neutral_element=float('inf') + ) + + def min(self, start=0, end=None): + """Returns min(arr[start], ..., arr[end])""" + + return super(MinSegmentTree, self).reduce(start, end) diff --git a/Results Single Integrator/Results set test/Code/N_try_100/utils.py b/Results Single Integrator/Results set test/Code/N_try_100/utils.py new file mode 100644 index 0000000..c426b89 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_100/utils.py @@ -0,0 +1,42 @@ +import tensorflow as tf +import numpy as np + +def array2tensor(array): + + return tf.expand_dims(tf.convert_to_tensor(array), 0) + +def de_normalize_tensor(state, state_norm_arr): + ''' Retrieve state from normalized state - tensor ''' + state_time = tf.concat([tf.zeros([state.shape[0], state.shape[1]-1]), tf.reshape((state[:,-1]+1)*state_norm_arr[-1]/2,[state.shape[0],1])],1) + state_no_time = state * state_norm_arr + mask = tf.concat([tf.ones([state.shape[0], state.shape[1]-1]), tf.zeros([state.shape[0], 1])],1) + state_not_norm = state_no_time * mask + state_time * (1 - mask) + + return state_not_norm + +def normalize_tensor(state, state_norm_arr): + ''' Retrieve state from normalized state - tensor ''' + state_norm_time = tf.concat([tf.zeros([state.shape[0], state.shape[1]-1]), tf.reshape(((state[:,-1]) / state_norm_arr[-1])*2 - 1,[state.shape[0],1])],1) + state_norm_no_time = state / state_norm_arr + mask = tf.concat([tf.ones([state.shape[0], state.shape[1]-1]), tf.zeros([state.shape[0], 1])],1) + state_norm = state_norm_no_time * mask + state_norm_time * (1 - mask) + + return state_norm + +def de_normalize(state, state_norm_arr): + ''' Retrieve state from normalized state ''' + state_not_norm = np.empty_like(state) + state_not_norm[:-1] = state[:-1] * state_norm_arr[:-1] + state_not_norm[-1] = (state[-1] + 1) * state_norm_arr[-1]/2 + + return state_not_norm + +def normalize(state, state_norm_arr): + ''' Normalize state ''' + state_norm = np.empty_like(state) + state_norm = state / state_norm_arr + state_norm[-1] = state_norm[-1] * 2 -1 + + return state_norm + + diff --git a/Results Single Integrator/Results set test/Code/N_try_5/NeuralNetwork.py b/Results Single Integrator/Results set test/Code/N_try_5/NeuralNetwork.py new file mode 100644 index 0000000..90f4127 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/NeuralNetwork.py @@ -0,0 +1,233 @@ +import math +import random +import numpy as np +import tensorflow as tf +from tensorflow.keras import layers, regularizers +from tf_siren import SinusodialRepresentationDense + +from utils import normalize_tensor + +class NN: + + def __init__(self, env, conf, w_S=0): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :param NH1: (int) 1st hidden layer size + :param NH2: (int) 2nd hidden layer size + :param kreg_l1_A : (float) Weight of L1 regularization in actor's network - kernel + :param kreg_l2_A : (float) Weight of L2 regularization in actor's network - kernel + :param breg_l1_A : (float) Weight of L2 regularization in actor's network - bias + :param breg_l2_A : (float) Weight of L2 regularization in actor's network - bias + :param kreg_l1_C : (float) Weight of L1 regularization in critic's network - kernel + :param kreg_l2_C : (float) Weight of L2 regularization in critic's network - kernel + :param breg_l1_C : (float) Weight of L1 regularization in critic's network - bias + :param breg_l2_C : (float) Weight of L2 regularization in critic's network - bias + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param NORMALIZE_INPUTS : (bool) Flag to normalize inputs (state) + :param state_norm_array : (float array) Array used to normalize states + :param MC : (bool) Flag to use MC or TD(n) + :param cost_weights_terminal : (float array) Running cost weights vector + :param cost_weights_running : (float array) Terminal cost weights vector + :param BATCH_SIZE : (int) Size of the mini-batch + :param dt : (float) Timestep + + :input w_S : (float) Sobolev-training weight + ''' + + self.env = env + self.conf = conf + + self.w_S = w_S + + self.MSE = tf.keras.losses.MeanSquaredError() + + return + + def create_actor(self): + ''' Create actor NN ''' + inputs = layers.Input(shape=(self.conf.nb_state,)) + + lay1 = layers.Dense(self.conf.NH1,kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(inputs) + leakyrelu1 = layers.LeakyReLU()(lay1) + lay2 = layers.Dense(self.conf.NH2, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(leakyrelu1) + leakyrelu2 = layers.LeakyReLU()(lay2) + outputs = layers.Dense(self.conf.nb_action, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l1_A,self.conf.kreg_l2_A),bias_regularizer=regularizers.l1_l2(self.conf.breg_l1_A,self.conf.breg_l2_A))(leakyrelu2) + + model = tf.keras.Model(inputs, outputs) + + return model + + def create_critic_elu(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = layers.Dense(16, activation='elu')(state_input) + state_out2 = layers.Dense(32, activation='elu')(state_out1) + out_lay1 = layers.Dense(256, activation='elu')(state_out2) + out_lay2 = layers.Dense(256, activation='elu')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_sine_elu(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = SinusodialRepresentationDense(64, activation='sine')(state_input) + state_out2 = layers.Dense(64, activation='elu')(state_out1) + out_lay1 = SinusodialRepresentationDense(128, activation='sine')(state_out2) + out_lay2 = layers.Dense(128, activation='elu')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_sine(self): + ''' Create critic NN - elu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = SinusodialRepresentationDense(64, activation='sine')(state_input) + state_out2 = SinusodialRepresentationDense(64, activation='sine')(state_out1) + out_lay1 = SinusodialRepresentationDense(128, activation='sine')(state_out2) + out_lay2 = SinusodialRepresentationDense(128, activation='sine')(out_lay1) + + outputs = layers.Dense(1)(out_lay2) + + model = tf.keras.Model([state_input], outputs) + + return model + + def create_critic_relu(self): + ''' Create critic NN - relu''' + state_input = layers.Input(shape=(self.conf.nb_state,)) + + state_out1 = layers.Dense(16, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(state_input) + leakyrelu1 = layers.LeakyReLU()(state_out1) + + state_out2 = layers.Dense(32, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu1) + leakyrelu2 = layers.LeakyReLU()(state_out2) + out_lay1 = layers.Dense(self.conf.NH1, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu2) + leakyrelu3 = layers.LeakyReLU()(out_lay1) + out_lay2 = layers.Dense(self.conf.NH2, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu3) + leakyrelu4 = layers.LeakyReLU()(out_lay2) + + outputs = layers.Dense(1, kernel_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C),bias_regularizer=regularizers.l1_l2(self.conf.kreg_l2_C,self.conf.kreg_l2_C))(leakyrelu4) + + model = tf.keras.Model([state_input], outputs) + + return model + + def eval(self, NN, input): + ''' Compute the output of a NN given an input ''' + if not tf.is_tensor(input): + input = tf.convert_to_tensor(input, dtype=tf.float32) + + if self.conf.NORMALIZE_INPUTS: + input = normalize_tensor(input, self.conf.state_norm_arr) + + return NN(input, training=True) + + def custom_logarithm(self,input): + # Calculate the logarithms based on the non-zero condition + positive_log = tf.math.log(tf.math.maximum(input, 1e-7) + 1) + negative_log = -tf.math.log(tf.math.maximum(-input, 1e-7) + 1) + + # Use the appropriate logarithm based on the condition + result = tf.where(input > 0, positive_log, negative_log) + + return result + + def compute_critic_grad(self, critic_model, target_critic, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, weights_batch): + ''' Compute the gradient of the critic NN ''' + with tf.GradientTape() as tape: + # Compute value function tail if TD(n) is used + if self.conf.MC: + reward_to_go_batch = partial_reward_to_go_batch + else: + target_values = self.eval(target_critic, state_next_rollout_batch) # Compute Value at next state after conf.nsteps_TD_N steps given by target critic + reward_to_go_batch = partial_reward_to_go_batch + (1-d_batch)*target_values # Compute batch of 1-step targets for the critic loss + + # Compute critic loss + if self.w_S != 0: + with tf.GradientTape() as tape2: + tape2.watch(state_batch) + critic_value = self.eval(critic_model, state_batch) + der_critic_value = tape2.gradient(critic_value, state_batch) + + critic_loss_v = self.MSE(reward_to_go_batch, critic_value, sample_weight=weights_batch) + critic_loss_der = self.MSE(self.custom_logarithm(dVdx_batch[:,:-1]), self.custom_logarithm(der_critic_value[:,:-1]), sample_weight=weights_batch) # dV/dt not computed and so not used in the update + + critic_loss = critic_loss_der + self.w_S*critic_loss_v + else: + critic_value = self.eval(critic_model, state_batch) + critic_loss = self.MSE(reward_to_go_batch, critic_value, sample_weight=weights_batch) + + # Compute the gradients of the critic loss w.r.t. critic's parameters + critic_grad = tape.gradient(critic_loss, critic_model.trainable_variables) + + return critic_grad, reward_to_go_batch, critic_value, self.eval(target_critic, state_batch) + + def compute_actor_grad(self, actor_model, critic_model, state_batch, term_batch, batch_size): + ''' Compute the gradient of the actor NN ''' + if batch_size == None: + batch_size = self.conf.BATCH_SIZE + + actions = self.eval(actor_model, state_batch) + + # Both take into account normalization, ds_next_da is the gradient of the dynamics w.r.t. policy actions (ds'_da) + state_next_tf, ds_next_da = self.env.simulate_batch(state_batch.numpy(), actions.numpy()) , self.env.derivative_batch(state_batch.numpy(), actions.numpy()) + + with tf.GradientTape() as tape: + tape.watch(state_next_tf) + critic_value_next = self.eval(critic_model,state_next_tf) + + # dV_ds' = gradient of V w.r.t. s', where s'=f(s,a) a=policy(s) + dV_ds_next = tape.gradient(critic_value_next, state_next_tf) + + cost_weights_terminal_reshaped = np.reshape(self.conf.cost_weights_terminal,[1,len(self.conf.cost_weights_terminal)]) + cost_weights_running_reshaped = np.reshape(self.conf.cost_weights_running,[1,len(self.conf.cost_weights_running)]) + with tf.GradientTape() as tape1: + tape1.watch(actions) + rewards_tf = self.env.reward_batch(term_batch.dot(cost_weights_terminal_reshaped) + (1-term_batch).dot(cost_weights_running_reshaped), state_batch.numpy(), actions) + + # dr_da = gradient of reward r(s,a) w.r.t. policy's action a + dr_da = tape1.gradient(rewards_tf, actions, unconnected_gradients=tf.UnconnectedGradients.ZERO) + + dr_da_reshaped = tf.reshape(dr_da, (batch_size, 1, self.conf.nb_action)) + + # dr_ds' + dV_ds' (note: dr_ds' = 0) + dQ_ds_next = tf.reshape(dV_ds_next, (batch_size, 1, self.conf.nb_state)) + + # (dr_ds' + dV_ds')*ds'_da + dQ_ds_next_da = tf.matmul(dQ_ds_next, ds_next_da) + + # (dr_ds' + dV_ds')*ds'_da + dr_da + dQ_da = dQ_ds_next_da + dr_da_reshaped + + # Now let's multiply -[(dr_ds' + dV_ds')*ds'_da + dr_da] by the actions a + # and then let's autodifferentiate w.r.t theta_A (actor NN's parameters) to finally get -dQ/dtheta_A + with tf.GradientTape() as tape: + tape.watch(actor_model.trainable_variables) + actions = self.eval(actor_model, state_batch) + + actions_reshaped = tf.reshape(actions,(batch_size,self.conf.nb_action,1)) + dQ_da_reshaped = tf.reshape(dQ_da,(batch_size,1,self.conf.nb_action)) + Q_neg = tf.matmul(-dQ_da_reshaped,actions_reshaped) + + # Also here we need a scalar so we compute the mean -Q across the batch + mean_Qneg = tf.math.reduce_mean(Q_neg) + + # Gradients of the actor loss w.r.t. actor's parameters + actor_grad = tape.gradient(mean_Qneg, actor_model.trainable_variables) + + return actor_grad \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_5/RL.py b/Results Single Integrator/Results set test/Code/N_try_5/RL.py new file mode 100644 index 0000000..fa60798 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/RL.py @@ -0,0 +1,233 @@ +import uuid +import math +import numpy as np +import tensorflow as tf + +class RL_AC: + def __init__(self, env, NN, conf, N_try): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :parma critic_type : (str) Activation function to use for the critic NN + :param LR_SCHEDULE : (bool) Flag to use a scheduler for the learning rates + :param boundaries_schedule_LR_C : (list) Boudaries of critic LR + :param values_schedule_LR_C : (list) Values of critic LR + :param boundaries_schedule_LR_A : (list) Boudaries of actor LR + :param values_schedule_LR_A : (list) Values of actor LR + :param CRITIC_LEARNING_RATE : (float) Learning rate for the critic network + :param ACTOR_LEARNING_RATE : (float) Learning rate for the policy network + :param fresh_factor : (float) Refresh factor + :param prioritized_replay_alpha : (float) α determines how much prioritization is used + :param prioritized_replay_eps : (float) It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero + :param UPDATE_LOOPS : (int array) Number of updates of both critic and actor performed every EP_UPDATE episodes + :param save_interval : (int) save NNs interval + :param env_RL : (bool) Flag RL environment + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param MC : (bool) Flag to use MC or TD(n) + :param nsteps_TD_N : (int) Number of lookahed steps if TD(n) is used + :param UPDATE_RATE : (float) Homotopy rate to update the target critic network if TD(n) is used + :param cost_weights_terminal : (float array) Running cost weights vector + :param cost_weights_running : (float array) Terminal cost weights vector + :param dt : (float) Timestep + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param NNs_path : (str) NNs path + :param NSTEPS : (int) Max episode length + + ''' + self.env = env + self.NN = NN + self.conf = conf + + self.N_try = N_try + + self.actor_model = None + self.critic_model = None + self.target_critic = None + self.actor_optimizer = None + self.critic_optimizer = None + + self.init_rand_state = None + self.NSTEPS_SH = 0 + self.control_arr = None + self.state_arr = None + self.ee_pos_arr = None + self.exp_counter = np.zeros(conf.REPLAY_SIZE) + + return + + def setup_model(self, recover_training=None): + ''' Setup RL model ''' + # Create actor, critic and target NNs + self.actor_model = self.NN.create_actor() + + if self.conf.critic_type == 'elu': + self.critic_model = self.NN.create_critic_elu() + self.target_critic = self.NN.create_critic_elu() + elif self.conf.critic_type == 'sine': + self.critic_model = self.NN.create_critic_sine() + self.target_critic = self.NN.create_critic_sine() + elif self.conf.critic_type == 'sine-elu': + self.critic_model = self.NN.create_critic_sine_elu() + self.target_critic = self.NN.create_critic_sine_elu() + else: + self.critic_model = self.NN.create_critic_relu() + self.target_critic = self.NN.create_critic_relu() + + # Set optimizer specifying the learning rates + if self.conf.LR_SCHEDULE: + # Piecewise constant decay schedule + self.CRITIC_LR_SCHEDULE = tf.keras.optimizers.schedules.PiecewiseConstantDecay(self.conf.boundaries_schedule_LR_C, self.conf.values_schedule_LR_C) + self.ACTOR_LR_SCHEDULE = tf.keras.optimizers.schedules.PiecewiseConstantDecay(self.conf.boundaries_schedule_LR_A, self.conf.values_schedule_LR_A) + self.critic_optimizer = tf.keras.optimizers.Adam(self.CRITIC_LR_SCHEDULE) + self.actor_optimizer = tf.keras.optimizers.Adam(self.ACTOR_LR_SCHEDULE) + else: + self.critic_optimizer = tf.keras.optimizers.Adam(self.conf.CRITIC_LEARNING_RATE) + self.actor_optimizer = tf.keras.optimizers.Adam(self.conf.ACTOR_LEARNING_RATE) + + # Set initial weights of the NNs + if recover_training is not None: + NNs_path_rec = str(recover_training[0]) + N_try = recover_training[1] + update_step_counter = recover_training[2] + self.actor_model.load_weights("{}/N_try_{}/actor_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + self.critic_model.load_weights("{}/N_try_{}/critic_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + self.target_critic.load_weights("{}/N_try_{}/target_critic_{}.h5".format(NNs_path_rec,N_try,update_step_counter)) + else: + self.target_critic.set_weights(self.critic_model.get_weights()) + + def update(self, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, term_batch, weights_batch, batch_size=None): + ''' Update both critic and actor ''' + # Update the critic backpropagating the gradients + critic_grad, reward_to_go_batch, critic_value, target_critic_value = self.NN.compute_critic_grad(self.critic_model, self.target_critic, state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, weights_batch) + self.critic_optimizer.apply_gradients(zip(critic_grad, self.critic_model.trainable_variables)) + + # Update the actor backpropagating the gradients + actor_grad = self.NN.compute_actor_grad(self.actor_model, self.critic_model, state_batch, term_batch, batch_size) + self.actor_optimizer.apply_gradients(zip(actor_grad, self.actor_model.trainable_variables)) + + return reward_to_go_batch, critic_value, target_critic_value + + @tf.function + def update_target(self, target_weights, weights): + ''' Update target critic NN ''' + tau = self.conf.UPDATE_RATE + for (a, b) in zip(target_weights, weights): + a.assign(b * tau + a * (1 - tau)) + + def learn_and_update(self, update_step_counter, buffer, ep): + ''' Sample experience and update buffer priorities and NNs ''' + for i in range(int(self.conf.UPDATE_LOOPS[ep])): + # Sample batch of transitions from the buffer + state_batch, partial_reward_to_go_batch, state_next_rollout_batch, dVdx_batch, d_batch, term_batch, weights_batch, batch_idxes = buffer.sample() + + # Update both critic and actor + reward_to_go_batch, critic_value, target_critic_value = self.update(state_batch, state_next_rollout_batch, partial_reward_to_go_batch, dVdx_batch, d_batch, term_batch, weights_batch) + + # Update buffer priorities + if self.conf.prioritized_replay_alpha != 0: + buffer.update_priorities(batch_idxes, reward_to_go_batch, critic_value, target_critic_value) + + # Update target critic + if not self.conf.MC: + self.update_target(self.target_critic.variables, self.critic_model.variables) + + update_step_counter += 1 + + # Plot rollouts and save the NNs every conf.log_rollout_interval-training episodes + if update_step_counter%self.conf.save_interval == 0: + self.RL_save_weights(update_step_counter) + + return update_step_counter + + def RL_Solve(self, TO_controls, TO_states, TO_step_cost): + ''' Solve RL problem ''' + ep_return = 0 # Initialize the return + rwrd_arr = np.empty(self.NSTEPS_SH+1) # Reward array + state_next_rollout_arr = np.zeros((self.NSTEPS_SH+1, self.conf.nb_state)) # Next state array + partial_reward_to_go_arr = np.empty(self.NSTEPS_SH+1) # Partial cost-to-go array + total_reward_to_go_arr = np.empty(self.NSTEPS_SH+1) # Total cost-to-go array + term_arr = np.zeros(self.NSTEPS_SH+1) # Episode-termination flag array + term_arr[-1] = 1 + done_arr = np.zeros(self.NSTEPS_SH+1) # Episode-MC-termination flag array + + # START RL EPISODE + self.control_arr = TO_controls # action clipped in TO + + if self.conf.env_RL: + for step_counter in range(self.NSTEPS_SH): + # Simulate actions and retrieve next state and compute reward + self.state_arr[step_counter+1,:], rwrd_arr[step_counter] = self.env.step(self.conf.cost_weights_running, self.state_arr[step_counter,:], self.control_arr[step_counter,:]) + + # Compute end-effector position + self.ee_pos_arr[step_counter+1,:] = self.env.get_end_effector_position(self.state_arr[step_counter+1, :]) + rwrd_arr[-1] = self.env.reward(self.conf.cost_weights_terminal, self.state_arr[-1,:]) + else: + self.state_arr, rwrd_arr = TO_states, -TO_step_cost + + ep_return = sum(rwrd_arr) + + # Store transition after computing the (partial) cost-to go when using n-step TD (from 0 to Monte Carlo) + for i in range(self.NSTEPS_SH+1): + # set final lookahead step depending on whether Monte Cartlo or TD(n) is used + if self.conf.MC: + final_lookahead_step = self.NSTEPS_SH + done_arr[i] = 1 + else: + final_lookahead_step = min(i+self.conf.nsteps_TD_N, self.NSTEPS_SH) + if final_lookahead_step == self.NSTEPS_SH: + done_arr[i] = 1 + else: + state_next_rollout_arr[i,:] = self.state_arr[final_lookahead_step+1,:] + + # Compute the partial and total cost to go + partial_reward_to_go_arr[i] = np.float32(sum(rwrd_arr[i:final_lookahead_step+1])) + total_reward_to_go_arr[i] = np.float32(sum(rwrd_arr[i:self.NSTEPS_SH+1])) + + return self.state_arr, partial_reward_to_go_arr, total_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, self.ee_pos_arr + + def RL_save_weights(self, update_step_counter='final'): + ''' Save NN weights ''' + self.actor_model.save_weights(self.conf.NNs_path+"/N_try_{}/actor_{}.h5".format(self.N_try,update_step_counter)) + self.critic_model.save_weights(self.conf.NNs_path+"/N_try_{}/critic_{}.h5".format(self.N_try,update_step_counter)) + self.target_critic.save_weights(self.conf.NNs_path+"/N_try_{}/target_critic_{}.h5".format(self.N_try,update_step_counter)) + + def create_TO_init(self, ep, ICS): + ''' Create initial state and initial controls for TO ''' + self.init_rand_state = ICS + + self.NSTEPS_SH = self.conf.NSTEPS - int(self.init_rand_state[-1]/self.conf.dt) + if self.NSTEPS_SH == 0: + return None, None, None, None, 0 + + # Initialize array to store RL state, control, and end-effector trajectories + self.control_arr = np.empty((self.NSTEPS_SH, self.conf.nb_action)) + self.state_arr = np.empty((self.NSTEPS_SH+1, self.conf.nb_state)) + self.ee_pos_arr = np.empty((self.NSTEPS_SH+1,3)) + + # Set initial state and end-effector position + self.state_arr[0,:] = self.init_rand_state + self.ee_pos_arr[0,:] = self.env.get_end_effector_position(self.state_arr[0, :]) + + # Initialize array to initialize TO state and control variables + init_TO_controls = np.zeros((self.NSTEPS_SH, self.conf.nb_action)) + init_TO_states = np.zeros(( self.NSTEPS_SH+1, self.conf.nb_state)) + + # Set initial state + init_TO_states[0,:] = self.init_rand_state + + # Simulate actor's actions to compute the state trajectory used to initialize TO state variables (use ICS for state and 0 for control if it is the first episode otherwise use policy rollout) + success_init_flag = 1 + for i in range(self.NSTEPS_SH): + if ep == 0: + init_TO_controls[i,:] = np.zeros(self.conf.nb_action) + else: + init_TO_controls[i,:] = tf.squeeze(self.NN.eval(self.actor_model, np.array([init_TO_states[i,:]]))).numpy() + init_TO_states[i+1,:] = self.env.simulate(init_TO_states[i,:],init_TO_controls[i,:]) + if np.isnan(init_TO_states[i+1,:]).any(): + success_init_flag = 0 + return None, None, None, None, success_init_flag + + return self.init_rand_state, init_TO_states, init_TO_controls, self.NSTEPS_SH, success_init_flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_5/TO.py b/Results Single Integrator/Results set test/Code/N_try_5/TO.py new file mode 100644 index 0000000..1933f7a --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/TO.py @@ -0,0 +1,201 @@ +import sys +import math +import casadi +import numpy as np +import pinocchio.casadi as cpin + +class TO_Casadi: + + def __init__(self, env, conf, env_TO, w_S=0): + ''' + :input env : (Environment instance) + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param u_min : (float array) Action lower bound array + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param dt : (float) Timestep + + :input system_id : (str) Id system + + :input w_S : (float) Sobolev-training weight + ''' + + self.env = env + self.conf = conf + + self.nx = conf.nx + self.nu = conf.na + + self.w_S = w_S + + self.CAMS = env_TO + + def TO_System_Solve(self, ICS_state, init_TO_states, init_TO_controls, T): + ''' Create and solbe TO casadi problem ''' + ### PROBLEM + opti = casadi.Opti() + + # The control models are stored as a collection of shooting nodes called running models, with an additional terminal model. + self.runningSingleModel = self.CAMS('running_model', self.conf) + runningModels = [ self.runningSingleModel for _ in range(T) ] + self.terminalModel = self.CAMS('terminal_model', self.conf) + + # Decision variables + xs = [ opti.variable(model.nx) for model in runningModels+[self.terminalModel] ] # state variable + us = [ opti.variable(model.nu) for model in runningModels ] # control variable + + # Roll out loop, summing the integral cost and defining the shooting constraints. + total_cost = 0 + + opti.subject_to(xs[0] == ICS_state[:-1]) + + for t in range(T): + x_next, r_cost = runningModels[t].step_fun(xs[t], us[t]) + opti.subject_to(xs[t + 1] == x_next ) + total_cost += r_cost + r_cost_final = self.terminalModel.cost(xs[-1], us[-1]) + total_cost += r_cost_final + + ### SOLVE + opti.minimize(total_cost) + + # Create warmstart + init_x_TO = [np.array(init_TO_states[i,:-1]) for i in range(T+1)] + init_u_TO = [np.array(init_TO_controls[i,:]) for i in range(T)] + + for x,xg in zip(xs,init_x_TO): opti.set_initial(x,xg) + for u,ug in zip(us,init_u_TO): opti.set_initial(u,ug) + + # Set solver options + opts = {'ipopt.linear_solver':'ma57', 'ipopt.sb': 'yes','ipopt.print_level': 0, 'print_time': 0} #, 'ipopt.max_iter': 500} + opti.solver("ipopt", opts) + + try: + opti.solve() + TO_states = np.array([ opti.value(x) for x in xs ]) + TO_controls = np.array([ opti.value(u) for u in us ]) + TO_total_cost = opti.value(total_cost) + TO_ee_pos_arr = np.empty((T+1,3)) + TO_step_cost = np.empty(T+1) + for n in range(T): + TO_ee_pos_arr[n,:] = np.reshape(runningModels[n].p_ee(TO_states[n,:]),-1) + TO_step_cost[n] = runningModels[n].cost(TO_states[n,:], TO_controls[n,:]) + TO_ee_pos_arr[-1,:] = np.reshape(self.terminalModel.p_ee(TO_states[-1,:]),-1) + TO_step_cost[-1] = self.terminalModel.cost(TO_states[-1,:], TO_controls[-1,:]) + success_flag = 1 + except: + print('ERROR in convergence, returning debug values') + TO_states = np.array([ opti.debug.value(x) for x in xs ]) + TO_controls = np.array([ opti.debug.value(u) for u in us ]) + TO_total_cost = None + TO_ee_pos_arr = None + TO_step_cost = None + success_flag = 0 + + return success_flag, TO_controls, TO_states, TO_ee_pos_arr, TO_total_cost, TO_step_cost + + def TO_Solve(self, ICS_state, init_TO_states, init_TO_controls, T): + ''' Retrieve TO problem solution and compute the value function derviative with respect to the state ''' + success_flag, TO_controls, TO_states, TO_ee_pos_arr, _, TO_step_cost = self.TO_System_Solve(ICS_state, init_TO_states, init_TO_controls, T) + if success_flag == 0: + return None, None, success_flag, None, None, None + + if self.w_S != 0: + # Compute V gradient w.r.t. x (no computation dV/dt) + dVdx = self.backward_pass(T+1, TO_states, TO_controls) + else: + dVdx = np.zeros((T+1, self.conf.nb_state)) + + # Add the last state component (time) + TO_states = np.concatenate((TO_states, init_TO_states[0,-1] + np.transpose(self.conf.dt*np.array([range(T+1)]))), axis=1) + + return TO_controls, TO_states, success_flag, TO_ee_pos_arr, TO_step_cost, dVdx + + def backward_pass(self, T, TO_states, TO_controls, mu=1e-9): + ''' Perform the backward-pass of DDP to obtain the derivatives of the Value function w.r.t x ''' + n = self.conf.nb_state-1 + m = self.conf.nb_action + + X_bar = np.zeros((T, n)) + for i in range(n): + X_bar[:,i] = [TO_states[t,i] for t in range(T)] + + U_bar = np.zeros((T-1, m)) + for i in range(m): + U_bar[:,i] = [TO_controls[t,i] for t in range(T-1)] + + # The task is defined by a quadratic cost: + # sum_{i=0}^T 0.5 x' l_{xx,i} x + l_{x,i} x + 0.5 u' l_{uu,i} u + l_{u,i} u + x' l_{xu,i} u + l_x = np.zeros((T, n)) + l_xx = np.zeros((T, n, n)) + l_u = np.zeros((T-1, m)) + l_uu = np.zeros((T-1, m, m)) + l_xu = np.zeros((T-1, n, m)) + + # The cost-to-go is defined by a quadratic function: 0.5 x' Q_{xx,i} x + Q_{x,i} x + ... + Q_xx = np.zeros((T-1, n, n)) + Q_x = np.zeros((T-1, n)) + Q_uu = np.zeros((T-1, m, m)) + Q_u = np.zeros((T-1, m)) + Q_xu = np.zeros((T-1, n, m)) + + x = casadi.SX.sym('x',n,1) + u = casadi.SX.sym('u',m,1) + + running_cost = -self.runningSingleModel.cost(x, u) + terminal_cost = -self.terminalModel.cost(x, u) + + running_cost_xx, running_cost_x = casadi.hessian(running_cost,x) + running_cost_uu, running_cost_u = casadi.hessian(running_cost,u) + running_cost_xu = casadi.jacobian(casadi.jacobian(running_cost,x),u) + terminal_cost_xx, terminal_cost_x = casadi.hessian(terminal_cost,x) + + fun_running_cost_x = casadi.Function('fun_running_cost_x', [x], [running_cost_x], ['x'], ['running_cost_x']) + fun_running_cost_xx = casadi.Function('fun_running_cost_xx', [x], [running_cost_xx], ['x'], ['running_cost_xx']) + fun_running_cost_xu = casadi.Function('fun_running_cost_xu', [x,u],[running_cost_xu], ['x','u'], ['running_cost_xu']) + fun_running_cost_u = casadi.Function('fun_running_cost_u', [u], [running_cost_u], ['u'], ['running_cost_u']) + fun_running_cost_uu = casadi.Function('fun_running_cost_uu', [u], [running_cost_uu], ['u'], ['running_cost_uu']) + fun_terminal_cost_x = casadi.Function('fun_terminal_cost_x', [x], [terminal_cost_x], ['x'], ['terminal_cost_x']) + fun_terminal_cost_xx = casadi.Function('fun_terminal_cost_xx',[x], [terminal_cost_xx], ['x'], ['terminal_cost_xx']) + + # The Value function is defined by a quadratic function: 0.5 x' V_{xx,i} x + V_{x,i} x + V_xx = np.zeros((T, n, n)) + V_x = np.zeros((T, n+1)) + + # Dynamics derivatives w.r.t. x and u + A = np.zeros((T-1, n, n)) + B = np.zeros((T-1, n, m)) + + # Initialize value function + l_x[-1,:], l_xx[-1,:,:] = np.reshape(fun_terminal_cost_x(X_bar[-1,:]),n), fun_terminal_cost_xx(X_bar[-1,:]) + V_xx[T-1,:,:] = l_xx[-1,:,:] + V_x[T-1,:-1] = l_x[-1,:] + + for i in range(T-2, -1, -1): + # Compute dynamics Jacobians + A[i,:,:], B[i,:,:] = self.env.augmented_derivative(X_bar[i,:], U_bar[i,:]) + + # Compute the gradient of the cost function at X=X_bar + l_x[i,:], l_xx[i,:,:] = np.reshape(fun_running_cost_x(X_bar[i,:]),n), fun_running_cost_xx(X_bar[i,:]) + l_u[i,:],l_uu[i,:,:] = np.reshape(fun_running_cost_u(U_bar[i,:]),m), fun_running_cost_uu(U_bar[i,:]) + l_xu[i,:,:] = fun_running_cost_xu(X_bar[i,:], U_bar[i,:]) + + # Compute regularized cost-to-go + Q_x[i,:] = l_x[i,:] + A[i,:,:].T @ V_x[i+1,:-1] + Q_u[i,:] = l_u[i,:] + B[i,:,:].T @ V_x[i+1,:-1] + Q_xx[i,:,:] = l_xx[i,:,:] + A[i,:,:].T @ V_xx[i+1,:,:] @ A[i,:,:] + Q_uu[i,:,:] = l_uu[i,:,:] + B[i,:,:].T @ V_xx[i+1,:,:] @ B[i,:,:] + Q_xu[i,:,:] = l_xu[i,:,:] + A[i,:,:].T @ V_xx[i+1,:,:] @ B[i,:,:] + + Qbar_uu = Q_uu[i,:,:] + mu*np.identity(m) + Qbar_uu_pinv = np.linalg.pinv(Qbar_uu) + + # Compute the derivative of the Value function w.r.t. x + V_x[i,:-1] = Q_x[i,:] - Q_xu[i,:,:] @ Qbar_uu_pinv @ Q_u[i,:] + V_xx[i,:] = Q_xx[i,:] - Q_xu[i,:,:] @ Qbar_uu_pinv @ Q_xu[i,:,:].T + + return V_x \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_5/conf_car.py b/Results Single Integrator/Results set test/Code/N_try_5/conf_car.py new file mode 100644 index 0000000..c4d72bb --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/conf_car.py @@ -0,0 +1,203 @@ +import math +import numpy as np + +system_id = 'car' + +''' CACTO parameters ''' +EP_UPDATE = 250 # Number of episodes before updating critic and actor +NUPDATES = 260000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 38000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 500 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 10000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 1e2 # Distance from target weight +w_u = 1e1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Car/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Car/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Car/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Car/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Car/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Car/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Car/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' # Link mass + +### State parameters +nb_state = 5 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 5 +na = 2 +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, -math.pi, -10, -3, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, math.pi, 10, 3, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, math.pi, 10, 3, int(NSTEPS*dt)]) # Array used to normalize states +# state: x, y, theta, v, a, t + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-10.0, 10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-10.0, -10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([12.0, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([12.0, -2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([15.0, 0.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +bound_actions = 0 +omega_lower_bound = -2 # Action lower bound +omega_upper_bound = 2 +jerk_lower_bound = -1 +jerk_upper_bound = 1 # Action upper bound +u_min = np.array([omega_lower_bound, jerk_lower_bound]) # Action lower bound vector +u_max = np.array([omega_upper_bound, jerk_upper_bound]) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_5/conf_car_park.py b/Results Single Integrator/Results set test/Code/N_try_5/conf_car_park.py new file mode 100644 index 0000000..05c66c1 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/conf_car_park.py @@ -0,0 +1,221 @@ +import math +import numpy as np + +system_id = 'car_park' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 260000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 38000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/2) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 10000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 0 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -10 # X coord center ellipse 1 +YC1 = 6.75 # Y coord center ellipse 1 +A1 = 17 # Width ellipse 1 +B1 = 4.5 # Height ellipse 1 +XC2 = 10 # X coord center ellipse 2 +YC2 = 6.75 # Y coord center ellipse 2 +A2 = 17 # Width ellipse 2 +B2 = 4.5 # Height ellipse 2 +XC3 = 0 # X coord center ellipse 2 +YC3 = -2 # Y coord center ellipse 2 +A3 = 40 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +L = 4.35 +W = 2 +L_delta = 2.63 +tau_delta = 1 + +check_points_BF = np.array([[-L/2, W/2], + [-L/2+L/3, W/2], + [-L/2+2/3*L, W/2], + [L/2, W/2], + [L/2, 0], + [L/2, -W/2], + [-L/2+2/3*L, -W/2], + [-L/2+L/3, -W/2], + [-L/2, -W/2], + [-L/2, 0]]) # Check points in BF # Check points in BF + +### Weigths +w_d = 1e2 # Distance from target weight +w_u = 1e1 # Control effort weight +w_peak = 1e6 # Target threshold weight +w_ob = 5e4 # Obstacle weight +w_v = 1e2 # Velocity weight +delta_bound = 2*np.pi/6 +w_delta_bound = 0 # Delta bound weight (not implemented) +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u, w_delta_bound]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, w_v, w_ob, w_ob, w_ob, 0, w_delta_bound]) # Terminal cost weights vector + +k_db = 50 + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 1 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = 0 # Target x position +y_des = 6.75 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Car Park/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Car Park/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Car Park/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Car Park/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Car Park/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Car Park/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Car Park/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' # Link mass + +### State parameters +nb_state = 5 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 5 +na = 2 + +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-10, 1.5, -math.pi/6, 0, 0, 0]) #1.5 #-2.5 # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 10, 3, math.pi/6, 0, 0, (NSTEPS-1)*dt]) #3.0 #10 # State upper bound initial configuration array +state_norm_arr = np.array([10, 3, math.pi, 10, math.pi/6, int(NSTEPS*dt)]) # Array used to normalize states +# state: x, y, theta, v, delta, t + +# initial configurations for plot.rollout() +init_states_sim = [np.array([-9.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-5.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-2.5 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([0.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([2.5 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([5.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([9.0 - L_delta, 2.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +acc_lower_bound = -3 # Action lower bound +acc_upper_bound = 3 +delta_dot_lower_bound = -1 +delta_dot_upper_bound = 1 # Action upper bound +u_min = np.array([acc_lower_bound, delta_dot_lower_bound]) # Action lower bound vector +u_max = np.array([acc_upper_bound, delta_dot_upper_bound]) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-11, 11], [-2.5, 10]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_5/conf_double_integrator.py b/Results Single Integrator/Results set test/Code/N_try_5/conf_double_integrator.py new file mode 100644 index 0000000..03b56ca --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/conf_double_integrator.py @@ -0,0 +1,213 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'double_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 50000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 18000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 200 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-4 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 1 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Double Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Double Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Double Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Double Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Double Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Double Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Double Integrator/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Robot upload data +URDF_FILENAME = "double_integrator.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + +q_init, v_init = np.array([-5, 0]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max) + +### State parameters +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, dt]) # State lower bound vector +x_init_min = np.array([-15, -15, -6, -6, dt]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, 6, 6, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, 6, 6, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, 10.0, 0.0, 0.0, 0.0]), + np.array([-10.0, -10.0, 0.0, 0.0, 0.0]), + np.array([12.0, 2.0, 0.0, 0.0, 0.0]), + np.array([12.0, -2.0, 0.0, 0.0, 0.0]), + np.array([15.0, 0.0, 0.0, 0.0, 0.0])] + +### Action parameters +nb_action = robot.na # Action size +tau_lower_bound = -2 # Action lower bound +tau_upper_bound = 2 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-15, 15], [-15, 15]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_5/conf_manipulator.py b/Results Single Integrator/Results set test/Code/N_try_5/conf_manipulator.py new file mode 100644 index 0000000..6b5d539 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/conf_manipulator.py @@ -0,0 +1,225 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'manipulator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 380000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 50000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network + nsteps_TD_N = int(NSTEPS/2) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 15000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 0 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 24000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' + +NH1 = 256 # 1st hidden layer size +NH2 = 256 # 2nd hidden layer size + +LR_SCHEDULE = 1 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 1e4 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, w_v, w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 50 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -20.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Manipulator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Manipulator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Manipulator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Manipulator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Manipulator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Manipulator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results Manipulator/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Robot upload data +URDF_FILENAME = "planar_manipulator_3dof.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + +q_init, v_init = np.array([math.pi, math.pi, math.pi]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max) + +### System configuration parameters +x_base = -7.0 # x coord base +y_base = 0.0 # y coord base + +### State parameters +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-math.pi, -math.pi, -math.pi, -math.pi/4, -math.pi/4, -math.pi/4, 0]) # State lower bound initial configuration vector +x_max = np.array([ np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ math.pi, math.pi, math.pi, math.pi/4, math.pi/4, math.pi/4, (NSTEPS-1)*dt]) # State upper bound initial configuration vector +state_norm_arr = np.array([15,15,15,10,10,10,int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([math.pi/4, -math.pi/8, -math.pi/8, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, math.pi/8, math.pi/8, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + #np.array([ 1.34565955, -2.39833441, 0.87800266, 0., 0., 0., 0. ]), + #np.array([-1.34565955, 2.39833441, -0.87800266, 0., 0., 0., 0. ]), + np.array([-1.55135003, 2.93707696, -1.3025857 , 0., 0., 0., 0. ]), + np.array([ 1.55135003, -2.93707696, 1.3025857 , 0., 0., 0., 0. ]), + np.array([-1.31811607, 2.63623214, -1.31811607, 0., 0., 0., 0. ]), + np.array([-0.98843209, 1.97686418, -0.98843209, 0., 0., 0., 0. ])] + #np.array([-1.05348883, 1.9057266 , -0.61849459, 0., 0., 0., 0. ])] + #np.array([ 1.05348883, -1.9057266 , 0.61849459, 0., 0., 0., 0. ])] + +### Action parameters +nb_action = robot.na # Action size +tau_lower_bound = -200 # Action lower bound +tau_upper_bound = 200 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-41, 31], [-35, 35]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_5/conf_single_integrator.py b/Results Single Integrator/Results set test/Code/N_try_5/conf_single_integrator.py new file mode 100644 index 0000000..02f767d --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/conf_single_integrator.py @@ -0,0 +1,200 @@ +import numpy as np + +system_id = 'single_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 100000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 25000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Single Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Single Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Single Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Single Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Single Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Single Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = 'set prova b' +NNs_path_rec = './Results Single Integrator/Results {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = 0 +update_step_counter_rec = 12000 + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + + + +### State parameters +nb_state = 2 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 2 +na = 2 +x_min = np.array([-np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0]), + np.array([10.0, 10.0, 0.0]), + np.array([-10.0, 10.0, 0.0]), + np.array([-10.0, -10.0, 0.0]), + np.array([12.0, 2.0, 0.0]), + np.array([12.0, -2.0, 0.0]), + np.array([15.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +tau_lower_bound = -6 # Action lower bound +tau_upper_bound = 6 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_5/conf_ur5.py b/Results Single Integrator/Results set test/Code/N_try_5/conf_ur5.py new file mode 100644 index 0000000..1521ac0 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/conf_ur5.py @@ -0,0 +1,227 @@ +import os +import math +import numpy as np +import pinocchio.casadi as cpin +from robot_utils import RobotWrapper, RobotSimulator + +system_id = 'ur5' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 380000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 50000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 64 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + # 2nd hidden layer size + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +# Obstacles parameters +XC1 = 0.0 # X coord center ellipse 1 +YC1 = 0.25 # Y coord center ellipse 1 +ZC1 = 0.2 +ell1_center = [XC1, YC1, ZC1] +A1 = 0.5 # Width ellipse 1 +B1 = 0.2 # Height ellipse 1 +C1 = 0.34 +XC2 = 0.2 # X coord center ellipse 2 +YC2 = 0.425 # Y coord center ellipse 2 +ZC2 = 0.2 +ell2_center = [XC2, YC2, ZC2] +A2 = 0.4 # Width ellipse 2 +B2 = 0.14 # Height ellipse 2 +C2 = 0.34 +XC3 = -0.2 # X coord center ellipse 2 +YC3 = 0.425 # Y coord center ellipse 2 +ZC3 = 0.2 +ell3_center = [XC3, YC3, ZC3] +A3 = 0.4 # Width ellipse 2 +B3 = 0.14 # Height ellipse 2 +C3 = 0.34 +obs_param = np.array([XC1, YC1, ZC1, XC2, YC2, ZC2, XC3, YC3, ZC3, A1, B1, C1, A2, B2, C2, A3, B3, C3]) # Obstacle parameters vector + +# Weigths +w_d = 100 # Distance from target weight +w_u = 1 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +# SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +# Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +# Target parameters +x_des = 0.0 # Target x position +y_des = 0.425 # Target y position +z_des = 0.2 +TARGET_STATE = np.array([x_des, y_des, z_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results UR5/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results UR5/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results UR5/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results UR5/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results UR5/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results UR5/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = None +NNs_path_rec = './Results UR5/Results set {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = None +update_step_counter_rec = None + + + +''' System parameters ''' +# Robot upload data +URDF_FILENAME = "ur5_robot.urdf" +modelPath = os.getcwd()+"/urdf/" + URDF_FILENAME +robot = RobotWrapper.BuildFromURDF(modelPath, [modelPath]) +nq = robot.nq +nv = robot.nv +nx = nq + nv +na = robot.na +cmodel = cpin.Model(robot.model) +cdata = cmodel.createData() +end_effector_frame_id = 'EE' + +# Dynamics parameters' +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(robot.na) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' +use_viewer = False +simulate_real_time = True +show_floor = False +PRINT_T = 1 # print every PRINT_N time steps +DISPLAY_T = 0.02 # update robot configuration in viewer every DISPLAY_N time steps +# CAMERA_TRANSFORM = [3.6914889812469482, 0.4583563506603241, -0.05435386672616005, 0.48037904500961304, 0.5339481830596924, 0.5137122273445129, 0.4692920446395874] +CAMERA_TRANSFORM = [0.36461642384529114, 0.8866147994995117, 2.579286575317383, 0.03584412857890129, 0.14833784103393555, 0.9481053948402405, 0.27893954515457153] + +q_init, v_init = np.array([0.,-math.pi/2,0.,0.,0.,0.]), np.zeros(robot.nv) +simu = RobotSimulator(robot, q_init, v_init, simulation_type, tau_coulomb_max, use_viewer, DISPLAY_T, CAMERA_TRANSFORM, show_floor) + +# State parameters +dt = 0.01 # Timestep +nb_state = robot.nq + robot.nv + 1 # State size (robot state size +1) +x_min = np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-math.pi, -math.pi, -math.pi, -math.pi, -math.pi, -math.pi, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, -math.pi/4, 0]) # State lower bound initial configuration vector +x_max = np.array([ np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ math.pi, math.pi, math.pi, math.pi, math.pi, math.pi, math.pi/4, math.pi/4, math.pi/4, math.pi/4, math.pi/4, math.pi/4, (NSTEPS-1)*dt]) # State upper bound initial configuration vector +state_norm_arr = np.array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([math.pi/4, -math.pi/8, -math.pi/8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, math.pi/8, math.pi/8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-3*math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([-math.pi/4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([math.pi, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])] + +# Action parameters +nb_action = robot.na # Action size +u_min = np.array([-150, -150, -150, -28, -28, -28]) # Action lower bound vector +u_max = np.array([150, 150, 150, 28, 28, 28]) # Action upper bound vector +w_b = 1/w_u + + +fig_ax_lim = np.array([[-3, 3], [-3, 3]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + +profile = 0 + +env_RL = 0 diff --git a/Results Single Integrator/Results set test/Code/N_try_5/environment.py b/Results Single Integrator/Results set test/Code/N_try_5/environment.py new file mode 100644 index 0000000..87bd97f --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/environment.py @@ -0,0 +1,816 @@ +import math +import mpmath +import random +import numpy as np +import tensorflow as tf +import pinocchio as pin + +from utils import * + +class Env: + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param simu : (RobotSimulator instance) + :param x_init_min : (float array) State lower bound initial configuration array + :param x_init_max : (float array) State upper bound initial configuration array + :param x_min : (float array) State lower bound vector + :param x_max : (float array) State upper bound vector + :param u_min : (float array) Action lower bound array + :param u_max : (float array) Action upper bound array + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + ''' + + self.conf = conf + + self.nq = conf.nq + self.nv = conf.nv + self.nx = conf.nx + self.nu = conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + def reset(self): + ''' Choose initial state uniformly at random ''' + state = np.zeros(self.conf.nb_state) + + time = random.uniform(self.conf.x_init_min[-1], self.conf.x_init_max[-1]) + for i in range(self.conf.nb_state-1): + state[i] = random.uniform(self.conf.x_init_min[i], self.conf.x_init_max[i]) + state[-1] = self.conf.dt*round(time/self.conf.dt) + + return state + + def check_ICS_feasible(self, state): + ''' Check if ICS is not feasible ''' + # check if ee is in the obstacles + p_ee = self.get_end_effector_position(state) + + ellipse1 = ((p_ee[0] - self.conf.XC1)**2) / ((self.conf.A1 / 2)**2) + ((p_ee[1] - self.conf.YC1)**2) / ((self.conf.B1 / 2)**2) + ellipse2 = ((p_ee[0] - self.conf.XC2)**2) / ((self.conf.A2 / 2)**2) + ((p_ee[1] - self.conf.YC2)**2) / ((self.conf.B2 / 2)**2) + ellipse3 = ((p_ee[0] - self.conf.XC3)**2) / ((self.conf.A3 / 2)**2) + ((p_ee[1] - self.conf.YC3)**2) / ((self.conf.B3 / 2)**2) + + feasible_flag = ellipse1 > 1 and ellipse2 > 1 and ellipse3 > 1 + + return feasible_flag + + def step(self, weights, state, action): + ''' Return next state and reward ''' + # compute next state + state_next = self.simulate(state, action) + + # compute reward + reward = self.reward(weights, state, action) + + return (state_next, reward) + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + # Simulate control action + self.conf.simu.simulate(np.copy(state[:-1]), action, self.conf.dt, 1) ### Explicit Euler ### + + # Return next state + state_next[:self.nq], state_next[self.nq:self.nx] = np.copy(self.conf.simu.q), np.copy(self.conf.simu.v) + state_next[-1] = state[-1] + self.conf.dt + + return state_next + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Create robot model in Pinocchio with q_init as initial configuration + q_init = state[:self.nq] + v_init = state[self.nq:self.nx] + + # Dynamics gradient w.r.t control (1st order euler) + pin.computeABADerivatives(self.conf.robot.model, self.conf.robot.data, np.copy(q_init), np.copy(v_init), action) + + Fu = np.zeros((self.nx+1, self.nu)) + Fu[self.nv:-1, :] = self.conf.robot.data.Minv + Fu[:self.nx, :] *= self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + q = state[:self.nq] + v = state[self.nq:self.nx] + + # Compute Jacobians for continuous time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + pin.computeABADerivatives(self.conf.robot.model, self.conf.robot.data, q, v, action) + + Fx[:self.nv, :self.nv] = 0.0 + Fx[:self.nv, self.nv:self.nx] = np.identity(self.nv) + Fx[self.nv:self.nx, :self.nv] = self.conf.robot.data.ddq_dq + Fx[self.nv:self.nx, self.nv:self.nx] = self.conf.robot.data.ddq_dv + Fu[self.nv:self.nx, :] = self.conf.robot.data.Minv + + # Convert them to discrete time + Fx = np.identity(self.conf.nb_state-1) + self.conf.dt * Fx + Fu *= self.conf.dt + + return Fx, Fu + + def simulate_batch(self, state, action): + ''' Simulate dynamics using tensors and compute its gradient w.r.t control. Batch-wise computation ''' + state_next = np.array([self.simulate(s, a) for s, a in zip(state, action)]) + + return tf.convert_to_tensor(state_next, dtype=tf.float32) + + def derivative_batch(self, state, action): + ''' Simulate dynamics using tensors and compute its gradient w.r.t control. Batch-wise computation ''' + Fu = np.array([self.derivative(s, a) for s, a in zip(state, action)]) + + return tf.convert_to_tensor(Fu, dtype=tf.float32) + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + q = state[:self.nq] + + RF = self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id) + + H = self.conf.robot.framePlacement(q, RF, recompute) + + p = H.translation + + return p + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + +class SingleIntegrator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[0,0] = self.conf.dt + Fu[1,1] = self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0], + [0, 1]]) + + Fu[0,0] = self.conf.dt + Fu[1,1] = self.conf.dt + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*action[0] + state_next[1] = state[1] + self.conf.dt*action[1] + state_next[2] = state[2] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + + return p + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class DoubleIntegrator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = [self.get_end_effector_position(state)[i] for i in range(2)] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = np.math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class Car(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[2,0] = self.conf.dt + Fu[4,1] = self.conf.dt + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0, -self.conf.dt*state[3]*math.sin(state[2]) -self.conf.dt**2*state[4]*math.sin(state[2])/2, self.conf.dt*math.cos(state[2]), self.conf.dt**2*math.cos(state[2])/2], + [0, 1, self.conf.dt*state[3]*math.cos(state[2]) +self.conf.dt**2*state[4]*math.cos(state[2])/2, self.conf.dt*math.sin(state[2]), self.conf.dt**2*math.sin(state[2])/2], + [0, 0, 1, 0, 0], + [0, 0, 0, 1, self.conf.dt], + [0, 0, 0, 0, 1]]) + + Fu[2,0] = self.conf.dt + Fu[4,1] = self.conf.dt + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*state[3]*tf.cos(state[2]) + self.conf.dt**2*state[4]*tf.cos(state[2])/2 + state_next[1] = state[1] + self.conf.dt*state[3]*tf.sin(state[2]) + self.conf.dt**2*state[4]*tf.sin(state[2])/2 + state_next[2] = state[2] + self.conf.dt*action[0] + state_next[3] = state[3] + self.conf.dt*state[4] + state_next[4] = state[4] + self.conf.dt*action[1] + state_next[5] = state[5] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + + return p + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class CarPark(Car): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + self.nx = conf.nx + self.nu = conf.na + + def check_ICS_feasible(self, state): + ''' Check if ICS is not feasible ''' + # check if ee is in the obstacles + x_ee, y_ee = self.get_end_effector_position(state)[:2] + theta_ee = state[2] + + for i in range(len(self.conf.check_points_BF)): + check_points_WF_i = np.array([x_ee, y_ee]) + np.array([[math.cos(theta_ee), -math.sin(theta_ee)], [math.sin(theta_ee), math.cos(theta_ee)]]).dot(self.conf.check_points_BF[i,:]) + obs_1 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC1,self.YC1,self.A1,self.B1) + obs_2 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC2,self.YC2,self.A2,self.B2) + obs_3 = self.obs_cost_fun(check_points_WF_i[0],check_points_WF_i[1],self.XC3,self.YC3,self.A3,self.B3) + + feasible_flag = obs_1 < 0.5 and obs_2 < 0.5 and obs_3 < 0.5 + if feasible_flag == 0: + return feasible_flag + + return feasible_flag + + def derivative(self, state, action): + ''' Compute the derivative ''' + # Dynamics gradient w.r.t control (1st order euler) + Fu = np.zeros((self.nx+1, self.nu)) + Fu[3,0] = self.conf.dt + Fu[4,1] = self.conf.dt/self.conf.tau_delta + + if self.conf.NORMALIZE_INPUTS: + Fu[:-1] *= (1/self.conf.state_norm_arr[:-1,None]) + + return Fu + + def augmented_derivative(self, state, action): + ''' Partial derivatives of system dynamics w.r.t. x ''' + # Compute Jacobians for discrete time dynamics + Fx = np.zeros((self.conf.nb_state-1,self.conf.nb_state-1)) + Fu = np.zeros((self.conf.nb_state-1,self.conf.nb_action)) + + Fx[:self.conf.nb_state-1,:self.conf.nb_state-1] = np.array([[1, 0, -self.conf.dt*state[3]*math.sin(state[2]), self.conf.dt*math.cos(state[2]), 0], + [0, 1, self.conf.dt*state[3]*math.cos(state[2]), self.conf.dt*math.sin(state[2]), 0], + [0, 0, 1, self.conf.dt*math.tan(state[4])/self.conf.L_delta, self.conf.dt*state[3]*mpmath.sec(state[4])**2/self.conf.L_delta], + [0, 0, 0, 1, 0], + [0, 0, 0, 0, 1]]) + + Fu[3,0] = self.conf.dt + Fu[4,1] = self.conf.dt/self.conf.tau_delta + + return Fx, Fu + + def simulate(self, state, action): + ''' Simulate dynamics ''' + state_next = np.zeros(self.nx+1) + + state_next[0] = state[0] + self.conf.dt*state[3]*math.cos(state[2]) + state_next[1] = state[1] + self.conf.dt*state[3]*math.sin(state[2]) + state_next[2] = state[2] + self.conf.dt*state[3]*math.tan(state[4])/self.conf.L_delta + state_next[3] = state[3] + self.conf.dt*action[0] + state_next[4] = state[4] + self.conf.dt*action[1]/self.conf.tau_delta + state_next[5] = state[5] + self.conf.dt + + return state_next + + def get_end_effector_position(self, state, recompute=True): + ''' Compute end-effector position ''' + p = np.zeros(3) + p[:2] = state[:2] + np.array([[math.cos(state[2]), -math.sin(state[2])], [math.sin(state[2]), math.cos(state[2])]]).dot(np.array([self.conf.L_delta/2,0])) + + return p + + def obs_cost_fun(self,x,y,x_step,y_step,Wx,Wy,fv=1,k=50): + k = self.conf.k_db + + term1 = 4 + 4 * (y - y_step + Wy/2)**2 * k**2 + term2 = 4 + 4 * (y - y_step - Wy/2)**2 * k**2 + term3 = 4 + 4 * (x - x_step + Wx/2)**2 * k**2 + term4 = 4 + 4 * (x - x_step - Wx/2)**2 * k**2 + obs_cost = (term1)**(-1/2) * fv * (-np.sqrt(term2) / 2 + (y - y_step - Wy/2) * k) * (term3)**(-1/2) * (term2)**(-1/2) * (np.sqrt(term1) / 2 + (y - y_step + Wy/2) * k) * (term4)**(-1/2) * (np.sqrt(term3) / 2 + (x - x_step + Wx/2) * k) * (-np.sqrt(term4) / 2 + (x - x_step - Wx/2) * k) + + return obs_cost + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = self.get_end_effector_position(state)[:2] + theta_ee = state[2] + + obs_cost = 0 + check_points_WF = np.dot(np.array([[np.cos(theta_ee), -np.sin(theta_ee)], [np.sin(theta_ee), np.cos(theta_ee)]]), self.conf.check_points_BF.T).T + np.array([x_ee, y_ee]) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC1, self.YC1, self.A1, self.B1)) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC2, self.YC2, self.A2, self.B2)) + obs_cost += np.sum(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC3, self.YC3, self.A3, self.B3)) + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 + 0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 + 0.1) - math.sqrt(0.1) - 0.1 )) + 1)/self.alpha2 + + # Term pensalizing the control effort + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*state[3]**2 - weights[3]*obs_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class Manipulator(Env): + ''' + :param cost_function_parameters : + ''' + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee = [self.get_end_effector_position(state)[i] for i in range(2)] + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + # Term penalizing the FINAL joint velocity + if weights[2] != 0: + vel_cost = state[self.nq:self.nx].dot(state[self.nq:self.nx]) + else: + vel_cost = 0 + + if action is not None: + u_cost = self.bound_control_cost(action) + else: + u_cost = 0 + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*vel_cost - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) #- weights[2]*vel_cost + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) + +class UR5(Env): + + metadata = { + "render_modes": [ + "human", "rgb_array" + ], + "render_fps": 4, + } + + def __init__(self, conf): + + self.conf = conf + + super().__init__(conf) + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.ZC1 = self.conf.obs_param[2] + self.XC2 = self.conf.obs_param[3] + self.YC2 = self.conf.obs_param[4] + self.ZC2 = self.conf.obs_param[5] + self.XC3 = self.conf.obs_param[6] + self.YC3 = self.conf.obs_param[7] + self.ZC3 = self.conf.obs_param[8] + + self.A1 = self.conf.obs_param[9] + self.B1 = self.conf.obs_param[10] + self.C1 = self.conf.obs_param[11] + self.A2 = self.conf.obs_param[12] + self.B2 = self.conf.obs_param[13] + self.C2 = self.conf.obs_param[14] + self.A3 = self.conf.obs_param[15] + self.B3 = self.conf.obs_param[16] + self.C3 = self.conf.obs_param[17] + + self.TARGET_STATE = self.conf.TARGET_STATE + + def reward(self, weights, state, action=None): + ''' Compute reward ''' + # End-effector coordinates + x_ee, y_ee, z_ee = self.get_end_effector_position(state) + + # Penalties for the ellipses representing the obstacle + ell1_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC1)**2)/((self.A1/2)**2) + ((y_ee-self.YC1)**2)/((self.B1/2)**2) + ((z_ee-self.ZC1)**2)/((self.C1/2)**2) - 1.0)) + 1)/self.alpha + ell2_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC2)**2)/((self.A2/2)**2) + ((y_ee-self.YC2)**2)/((self.B2/2)**2) + ((z_ee-self.ZC2)**2)/((self.C2/2)**2) - 1.0)) + 1)/self.alpha + ell3_cost = math.log(math.exp(self.alpha*-(((x_ee-self.XC3)**2)/((self.A3/2)**2) + ((y_ee-self.YC3)**2)/((self.B3/2)**2) + ((z_ee-self.ZC3)**2)/((self.C3/2)**2) - 1.0)) + 1)/self.alpha + + # Term pushing the agent to stay in the neighborhood of target + peak_rew = math.log(math.exp(self.alpha2*-(math.sqrt((x_ee-self.TARGET_STATE[0])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((y_ee-self.TARGET_STATE[1])**2 +0.1) - math.sqrt(0.1) - 0.1 + math.sqrt((z_ee-self.TARGET_STATE[2])**2 +0.1) - math.sqrt(0.1) - 0.1)) + 1)/self.alpha2 + + if action is not None: + u_cost = action.dot(action) + else: + u_cost = 0 + + # Term penalizing the FINAL joint velocity + vel_cost = state[self.nq:self.nx].dot(state[self.nq:self.nx]) + + dist_cost = (x_ee-self.TARGET_STATE[0])**2 + (y_ee-self.TARGET_STATE[1])**2 + (z_ee-self.TARGET_STATE[2])**2 + + r = self.scale*(- weights[0]*dist_cost + weights[1]*peak_rew - weights[2]*vel_cost - weights[3]*ell1_cost - weights[4]*ell2_cost - weights[5]*ell3_cost - weights[6]*u_cost + self.offset) + + return r + + def reward_batch(self, weights, state, action): + ''' Compute reward using tensors. Batch-wise computation ''' + partial_reward = np.array([self.reward(w, s) for w, s in zip(weights, state)]) + + # Redefine action-related cost in tensorflow version + u_cost = tf.reduce_sum((action**2 + self.conf.w_b*(action/self.conf.u_max)**10),axis=1) + + r = self.scale*(- weights[:,6]*u_cost) + tf.convert_to_tensor(partial_reward, dtype=tf.float32) + + return tf.reshape(r, [r.shape[0], 1]) diff --git a/Results Single Integrator/Results set test/Code/N_try_5/environment_TO.py b/Results Single Integrator/Results set test/Code/N_try_5/environment_TO.py new file mode 100644 index 0000000..0cf4b63 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/environment_TO.py @@ -0,0 +1,778 @@ +import sys +import math +import casadi +import numpy as np +import pinocchio.casadi as cpin + +class SingleIntegrator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + p_ee[-1] = 0 + + return p_ee + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*u[0] + x_next[1] = x[1] + self.conf.dt*u[1] + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class DoubleIntegrator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class Car_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + p_ee[-1] = 0 + + return p_ee + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*x[3]*casadi.cos(x[2]) + self.conf.dt**2*x[4]*casadi.cos(x[2])/2 + x_next[1] = x[1] + self.conf.dt*x[3]*casadi.sin(x[2]) + self.conf.dt**2*x[4]*casadi.sin(x[2])/2 + x_next[2] = x[2] + self.conf.dt*u[0] + x_next[3] = x[3] + self.conf.dt*x[4] + x_next[4] = x[4] + self.conf.dt*u[1] + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class CarPark_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nx = self.conf.nx + self.nu = self.conf.na + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + self.p_ee = casadi.Function('p_ee', [cx], [self.get_end_effector_position_fun(cx)]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def get_end_effector_position_fun(self, cx): + p_ee = casadi.SX(3,1) + p_ee[:2] = cx[:2] + np.array([[np.cos(cx[2]), -np.sin(cx[2])], [np.sin(cx[2]), np.cos(cx[2])]]).dot(np.array([self.conf.L_delta/2,0])) + p_ee[-1] = 0 + + return p_ee + + def obs_cost_fun(self, x, y, x_clip, y_clip, Wx, Wy, fv=1, k=50): + k = self.conf.k_db + obs_cost = (4 + 4 * (y - y_clip + Wy / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * fv * (-np.sqrt(4 + 4 * (y - y_clip - Wy / 2) ** 2 * k ** 2) / 2 + (y - y_clip - Wy / 2) * k) * (4 + 4 * (x - x_clip + Wx / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (4 + 4 * (y - y_clip - Wy / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (np.sqrt(4 + 4 * (y - y_clip + Wy / 2) ** 2 * k ** 2) / 2 + (y - y_clip + Wy / 2) * k) * (4 + 4 * (x - x_clip - Wx / 2) ** 2 * k ** 2) ** (-0.1e1 / 0.2e1) * (np.sqrt(4 + 4 * (x - x_clip + Wx / 2) ** 2 * k ** 2) / 2 + (x - x_clip + Wx / 2) * k) * (-np.sqrt(4 + 4 * (x - x_clip - Wx / 2) ** 2 * k ** 2) / 2 + (x - x_clip - Wx / 2) * k) + + return obs_cost + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def rotation_matrix(self, angle): + ''' Compute the 2x2 rotation matrix for a given angle ''' + cos_theta = casadi.cos(angle) + sin_theta = casadi.sin(angle) + + rotation_mat = casadi.vertcat(casadi.horzcat(cos_theta, -sin_theta), + casadi.horzcat(sin_theta, cos_theta)) + return rotation_mat + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee_tmp = self.p_ee(x) + p_ee = p_ee_tmp[:2] + theta_ee = x[2] + + ### Penalties representing the obstacle ### + obs_cost = 0 + check_points_WF = casadi.mtimes(self.rotation_matrix(theta_ee), self.conf.check_points_BF.T).T + casadi.repmat(p_ee.T, self.conf.check_points_BF.shape[0], 1) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC1, self.YC1, self.A1, self.B1)) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC2, self.YC2, self.A2, self.B2)) + obs_cost += casadi.sum1(self.obs_cost_fun(check_points_WF[:, 0], check_points_WF[:, 1], self.XC3, self.YC3, self.A3, self.B3)) + + ### Control effort term ### + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 2*0.1 - 2*np.sqrt(0.1))) + 1)/self.alpha2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*x[3]**2 + self.weights[3]*obs_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + x_next = casadi.SX(self.conf.nx,1) + x_next[0] = x[0] + self.conf.dt*x[3]*casadi.cos(x[2]) + x_next[1] = x[1] + self.conf.dt*x[3]*casadi.sin(x[2]) + x_next[2] = x[2] + self.conf.dt*x[3]*casadi.tan(x[4])/self.conf.L_delta + x_next[3] = x[3] + self.conf.dt*u[0] + x_next[4] = x[4] + self.conf.dt*u[1]/self.conf.tau_delta + + return x_next + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class Manipulator_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.XC2 = self.conf.obs_param[2] + self.YC2 = self.conf.obs_param[3] + self.XC3 = self.conf.obs_param[4] + self.YC3 = self.conf.obs_param[5] + self.A1 = self.conf.obs_param[6] + self.B1 = self.conf.obs_param[7] + self.A2 = self.conf.obs_param[8] + self.B2 = self.conf.obs_param[9] + self.A3 = self.conf.obs_param[10] + self.B3 = self.conf.obs_param[11] + + self.x_des = self.conf.TARGET_STATE[0] + self.y_des = self.conf.TARGET_STATE[1] + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def bound_control_cost(self, action): + u_cost = 0 + for i in range(self.conf.nb_action): + u_cost += action[i]*action[i] + self.conf.w_b*(action[i]/self.conf.u_max[i])**10 + + return u_cost + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC1)**2)/((self.A1/2)**2) + ((p_ee[1]-self.YC1)**2)/((self.B1/2)**2) - 1.0)) + 1)/self.alpha) + ell2_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC2)**2)/((self.A2/2)**2) + ((p_ee[1]-self.YC2)**2)/((self.B2/2)**2) - 1.0)) + 1)/self.alpha) + ell3_cost = (np.log(np.exp(self.alpha*-(((p_ee[0]-self.XC3)**2)/((self.A3/2)**2) + ((p_ee[1]-self.YC3)**2)/((self.B3/2)**2) - 1.0)) + 1)/self.alpha) + + ### Control effort term ### + if u is not None: + u_cost = self.bound_control_cost(u) + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.x_des)**2 + (p_ee[1]-self.y_des)**2 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.alpha2*-(np.sqrt((p_ee[0]-self.x_des)**2 +0.1) - 0.1 + np.sqrt((p_ee[1]-self.y_des)**2 +0.1) - 0.1 -2*np.sqrt(0.1))) + 1)/self.alpha2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost + +class UR5_CAMS: + def __init__(self, name, conf): + ''' + :name : (str) Name of the casadi-model (either 'running' or 'terminal') + + :input conf : (Configuration file) + + :param robot : (RobotWrapper instance) + :param cmodel : (Casadi-Pinocchio instance) + :param cdata : (Casadi-Pinocchio model data) + :param dt : (float) Timestep + :param end_effector_frame_id : (str) Name of EE-frame + + # Cost function parameters + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + :param cost_weights_running : (float array) Running cost weights vector + :param cost_weights_terminal : (float array) Terminal cost weights vector + ''' + self.name = name + self.conf = conf + + self.nq = self.conf.cmodel.nq + self.nv = self.conf.cmodel.nv + self.nx = self.nq+self.nv + self.nu = self.conf.cmodel.nv + + # Rename reward parameters + self.offset = self.conf.cost_funct_param[0] + self.scale = self.conf.cost_funct_param[1] + + self.alpha = self.conf.soft_max_param[0] + self.alpha2 = self.conf.soft_max_param[1] + + self.XC1 = self.conf.obs_param[0] + self.YC1 = self.conf.obs_param[1] + self.ZC1 = self.conf.obs_param[2] + self.XC2 = self.conf.obs_param[3] + self.YC2 = self.conf.obs_param[4] + self.ZC2 = self.conf.obs_param[5] + self.XC3 = self.conf.obs_param[6] + self.YC3 = self.conf.obs_param[7] + self.ZC3 = self.conf.obs_param[8] + + self.A1 = self.conf.obs_param[9] + self.B1 = self.conf.obs_param[10] + self.C1 = self.conf.obs_param[11] + self.A2 = self.conf.obs_param[12] + self.B2 = self.conf.obs_param[13] + self.C2 = self.conf.obs_param[14] + self.A3 = self.conf.obs_param[15] + self.B3 = self.conf.obs_param[16] + self.C3 = self.conf.obs_param[17] + + self.TARGET_STATE = self.conf.TARGET_STATE + + # The self.xdot will be a casadi function mapping: state,control -> [velocity,acceleration] + cx = casadi.SX.sym("x",self.nx,1) + cu = casadi.SX.sym("u",self.nu,1) + + self.x_next = casadi.Function('x_next', [cx, cu], [self.simulate_fun(cx,cu)]) + + cpin.framesForwardKinematics(self.conf.cmodel, self.conf.cdata,cx[:self.nq]) + self.p_ee = casadi.Function('p_ee', [cx], [self.conf.cdata.oMf[self.conf.robot.model.getFrameId(self.conf.end_effector_frame_id)].translation]) + + if self.name == 'running_model': + self.weights = np.copy(self.conf.cost_weights_running) + elif self.name == 'terminal_model': + self.weights = np.copy(self.conf.cost_weights_terminal) + else: + print("The model can be either 'running_model' or 'terminal_model'") + import sys + sys.exit() + + self.cost = casadi.Function('cost', [cx,cu], [self.cost_fun(cx,cu)]) + + def cost_fun(self, x, u): + ''' Compute cost ''' + p_ee = self.p_ee(x) + + ### Penalties representing the obstacle ### + ell1_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC1)**2)/((self.conf.A1/2)**2) + ((p_ee[1]-self.conf.YC1)**2)/((self.conf.B1/2)**2) + ((p_ee[2]-self.conf.ZC1)**2)/((self.conf.C1/2)**2) - 1.0)) + 1)/self.conf.alpha + ell2_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC2)**2)/((self.conf.A2/2)**2) + ((p_ee[1]-self.conf.YC2)**2)/((self.conf.B2/2)**2) + ((p_ee[2]-self.conf.ZC2)**2)/((self.conf.C2/2)**2) - 1.0)) + 1)/self.conf.alpha + ell3_cost = np.log(np.exp(self.conf.alpha*-(((p_ee[0]-self.conf.XC3)**2)/((self.conf.A3/2)**2) + ((p_ee[1]-self.conf.YC3)**2)/((self.conf.B3/2)**2) + ((p_ee[2]-self.conf.ZC3)**2)/((self.conf.C3/2)**2) - 1.0)) + 1)/self.conf.alpha + + ### Control effort term ### + u_cost = 0 + for i in range(self.nu): + u_cost += u[i]**2 + self.conf.w_b*(u[i]/self.conf.u_max[i])**10 + + ### Distence to target term (log valley centered at target) ### + peak_rew = np.log(np.exp(self.conf.alpha2*-(np.sqrt((p_ee[0]-self.conf.TARGET_STATE[0])**2 +0.1) - np.sqrt(0.1) - 0.1 + np.sqrt((p_ee[1]-self.conf.TARGET_STATE[1])**2 +0.1) - np.sqrt(0.1) - 0.1 + np.sqrt((p_ee[2]-self.conf.TARGET_STATE[2])**2 +0.1) - np.sqrt(0.1) - 0.1)) + 1)/self.conf.alpha2 + + ### Distence to target term (quadratic term) ### + dist_cost = (p_ee[0]-self.conf.TARGET_STATE[0])**2 + (p_ee[1]-self.conf.TARGET_STATE[1])**2 + (p_ee[2]-self.conf.TARGET_STATE[2])**2 + + ### Terminal cost on final velocity ### + v_cost = 0 + for i in range(self.nv): + v_cost += x[i+self.nq]**2 + + cost = self.scale*(self.weights[0]*dist_cost - self.weights[1]*peak_rew + self.weights[2]*v_cost + self.weights[3]*ell1_cost + self.weights[4]*ell2_cost + self.weights[5]*ell3_cost + self.weights[6]*u_cost - self.offset) + + return cost + + def simulate_fun(self, x, u): + ''' Integrate dynamics ''' + a = cpin.aba(self.conf.cmodel,self.conf.cdata,x[:self.nq],x[self.nq:],u) + + if self.conf.integration_scheme == 'SI-Euler': + F = casadi.vertcat(x[self.nq:]+self.dt*a, a) + elif self.conf.integration_scheme == 'E-Euler': + F = casadi.vertcat(x[self.nq:], a) + x_next = x + self.conf.dt*F + + return x_next + + def step_fun(self, x, u): + ''' Return next state and cost ''' + cost = self.cost(x, u) + + x_next = self.x_next(x,u) + + return x_next, cost \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_5/generate_tests_set_script.py b/Results Single Integrator/Results set test/Code/N_try_5/generate_tests_set_script.py new file mode 100644 index 0000000..4c5f781 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/generate_tests_set_script.py @@ -0,0 +1,26 @@ +### Create file to be executed ### +import os +import shutil + +system_id = 'car' +system_id_short = 'C' + +n_runs = 10 + +w_S = 1e-3 +info = '{}'.format(1) + +offset = 0 if w_S ==0 else 100 + +seed_list = [29556, 5280, 739, 92, 10, 7298, 14, 264, 22135, 342] + +shutil.copy('Template.sh', '{}{}.sh'.format(system_id_short,info)) +with open('{}{}.sh'.format(system_id_short,info), 'a') as f: + for i in range(1,1 + n_runs): + f.write("nohup python3 -u main.py --system-id='{}' --test-n={} --seed={} --w-S={} > out/{}{}.txt &\n".format(system_id,offset+i,seed_list[i-1], w_S, system_id_short,offset+i)) + f.write("\necho 'Running scripts in parallel'\n") + f.write("echo 'PID of this script is:' $$\n") + f.write("wait\n") + f.write("echo 'Script done running'\n") + +os.makedirs('out', exist_ok=True) \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_5/main.py b/Results Single Integrator/Results set test/Code/N_try_5/main.py new file mode 100644 index 0000000..8121e8c --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/main.py @@ -0,0 +1,254 @@ +import os +import sys +import time +import shutil +import random +import argparse +import importlib +import numpy as np +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # {'0' -> show all logs, '1' -> filter out info, '2' -> filter out warnings} +import tensorflow as tf +from multiprocessing import Pool +from RL import RL_AC +from TO import TO_Casadi +from plot_utils import PLOT +from NeuralNetwork import NN +from replay_buffer import PrioritizedReplayBuffer, ReplayBuffer + +def parse_args(): + ''' Parse the arguments for CACTO training ''' + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + + parser.add_argument('--test-n', type=int, default=0, + help="Test number") + + parser.add_argument('--seed', type=int, default=0, + help="random and tf.random seed") + + parser.add_argument('--system-id', type=str, default='single_integrator', + choices=["single_integrator", "double_integrator", "car", "car_park", "manipulator", "ur5"], + help="System-id (single_integrator, double_integrator, car, manipulator, ur5") + + parser.add_argument('--recover-training-flag', type=bool, default=False, + choices=["True", "False"], + help="Flag to recover training") + ### Not tested ### + #parser.add_argument('--GPU-flag', type=bool, default=False, + # choices=["True", "False"], + # help="Flag to use GPU") + + parser.add_argument('--nb-cpus', type=int, default=10, + help="Number of TO problems solved in parallel") + + parser.add_argument('--w-S', type=float, default=0, + help="Sobolev training - weight of the value related error") + + args = parser.parse_args() + dict_args = vars(args) + + return dict_args + + + + +if __name__ == '__main__': + + args = parse_args() + + ############### Input ############### + N_try = args['test_n'] + + if args['seed'] == None: + seed = random.randint(1,100000) + else: + seed = args['seed'] + tf.random.set_seed(seed) # Set tensorflow seed + random.seed(seed) # Set random seed + + system_id = args['system_id'] + + recover_training_flag = args['recover_training_flag'] + + ### Not tested ### + #GPU_flag = args['GPU_flag'] + #if GPU_flag: + # os.environ["CUDA_VISIBLE_DEVICES"]="-1" + #print(tf.config.experimental.list_physical_devices('GPU')) + + nb_cpus = args['nb_cpus'] + + w_S = args['w_S'] + ######################################################### + + + + # Import configuration file and environment file + system_map = { + 'single_integrator': ('conf_single_integrator', 'SingleIntegrator', 'SingleIntegrator_CAMS'), + 'double_integrator': ('conf_double_integrator', 'DoubleIntegrator', 'DoubleIntegrator_CAMS'), + 'car': ('conf_car', 'Car', 'Car_CAMS'), + 'car_park': ('conf_car_park', 'CarPark', 'CarPark_CAMS'), + 'manipulator': ('conf_manipulator', 'Manipulator', 'Manipulator_CAMS'), + 'ur5': ('conf_ur5', 'UR5', 'UR5_CAMS') + } + try: + conf_module, env_class, env_TO_class = system_map[system_id] + conf = importlib.import_module(conf_module) + Environment = getattr(importlib.import_module('environment'), env_class) + Environment_TO = getattr(importlib.import_module('environment_TO'), env_TO_class) + except KeyError: + print('System {} not found'.format(system_id)) + sys.exit() + + # Create folders to store the results and the trained NNs + for path in conf.path_list: + os.makedirs(path + '/N_try_{}'.format(N_try), exist_ok=True) + os.makedirs(conf.Config_path, exist_ok=True) + + # Save configuration + params = [p for p in conf.__dict__ if not p.startswith("__")] + with open(conf.Config_path + '/config{}.txt'.format(N_try), 'w') as f: + for p in params: + f.write('{} = {}\n'.format(p, conf.__dict__[p])) + f.write('Seed = {}\n'.format(seed)) + f.write('w_S = {}'.format(w_S)) + + shutil.copy('{}.py'.format(conf_module), conf.Config_path + '/' + conf_module + '_{}.py'.format(N_try)) + with open(conf.Config_path + '/' + conf_module + '_{}.py'.format(N_try), 'a') as f: + f.write('\n\n# {}'.format(args)) + + # Copy all file with .py extension from /mydir to /mydestdir + for file in os.listdir("./"): + if file.endswith(".py"): + shutil.copy(os.path.join("./", file), os.path.join(conf.Code_path + '/N_try_{}'.format(N_try), file)) + + # Create empty txt file in Log_path to store the test info + open(conf.Log_path + '/info.txt', 'a').close() + + + + ### Create instances of the used classes ### + env = Environment(conf) # Create environment instances + env_TO = Environment_TO + NN_inst = NN(env, conf, w_S) # Create NN instance + TrOp = TO_Casadi(env, conf, env_TO, w_S) # Create TO instance + RLAC = RL_AC(env, NN_inst, conf, N_try) # Create RL instance + buffer = ReplayBuffer(conf) if conf.prioritized_replay_alpha == 0 else PrioritizedReplayBuffer(conf) # Create an empty (prioritized) replay buffer + plot_fun = PLOT(N_try, env, NN_inst, conf) # Create PLOT instance + + # Set initial weights of the NNs, initialize the counter of the updates and setup NN models + if recover_training_flag: + recover_training = np.array([conf.NNs_path_rec, conf.N_try_rec, conf.update_step_counter_rec]) + update_step_counter = conf.update_step_counter_rec + + RLAC.setup_model(recover_training) + else: + update_step_counter = 0 + + RLAC.setup_model() + + # Save initial weights of the NNs + RLAC.RL_save_weights(update_step_counter) + + # Plot initial rollouts + plot_fun.plot_traj_from_ICS(np.array(conf.init_states_sim), TrOp, RLAC, update_step_counter=update_step_counter,steps=conf.NSTEPS, init=0) + + # Initialize arrays to store the reward history of each episode and the average reward history of last 100 episodes + ep_arr_idx = 0 + ep_reward_arr = np.zeros(conf.NEPISODES-ep_arr_idx)*np.nan + + def compute_sample(args): + ''' Create samples solving TO problems starting from given ICS ''' + ep = args[0] + ICS = args[1] + + # Create initial TO # + init_rand_state, init_TO_states, init_TO_controls, NSTEPS_SH, success_init_flag = RLAC.create_TO_init(ep, ICS) + if success_init_flag == 0: + return None + + # Solve TO problem # + TO_controls, TO_states, success_flag, TO_ee_pos_arr, TO_step_cost, dVdx = TrOp.TO_Solve(init_rand_state, init_TO_states, init_TO_controls, NSTEPS_SH) + if success_flag == 0: + return None + + # Collect experiences + state_arr, partial_reward_to_go_arr, total_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, RL_ee_pos_arr = RLAC.RL_Solve(TO_controls, TO_states, TO_step_cost) + + if conf.env_RL == 0: + RL_ee_pos_arr = TO_ee_pos_arr + + return NSTEPS_SH, TO_controls, TO_ee_pos_arr, dVdx, state_arr.tolist(), partial_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, RL_ee_pos_arr + + def create_unif_TO_init(n_UICS=1): + ''' Create n uniformely distributed ICS ''' + # Create ICS TO # + init_rand_state = env.reset() + + return init_rand_state + + + + ### START TRAINING ### + if conf.profile: + import cProfile, pstats + + profiler = cProfile.Profile() + profiler.enable() + + time_start = time.time() + + for ep in range(conf.NLOOPS): + # Generate and store conf.EP_UPDATE random-uniform ICS + with Pool(nb_cpus) as p: + init_rand_state = p.map(create_unif_TO_init, range(conf.EP_UPDATE)) + + # Generate samples + with Pool(nb_cpus) as p: + tmp = p.map(compute_sample, zip(ep*np.ones(conf.EP_UPDATE), init_rand_state)) + + # Remove unsuccessful TO problems and update EP_UPDATE + tmp = [x for x in tmp if x is not None] + NSTEPS_SH, TO_controls, ee_pos_arr_TO, dVdx, state_arr, partial_reward_to_go_arr, state_next_rollout_arr, done_arr, rwrd_arr, term_arr, ep_return, ee_pos_arr_RL = zip(*tmp) + + # Update the buffer + buffer.add(state_arr, partial_reward_to_go_arr, state_next_rollout_arr, dVdx, done_arr, term_arr) + + # Update NNs + update_step_counter = RLAC.learn_and_update(update_step_counter, buffer, ep) + + # plot Critic value function + #plot_fun.plot_Critic_Value_function(RLAC.critic_model, update_step_counter, system_id) ### + + # Plot rollouts and state and control trajectories + if update_step_counter%conf.plot_rollout_interval_diff_loc == 0 or system_id == 'single_integrator' or system_id == 'double_integrator' or system_id == 'car_park' or system_id == 'car' or system_id == 'manipulator': + print("System: {} - N_try = {}".format(conf.system_id, N_try)) + plot_fun.plot_Critic_Value_function(RLAC.critic_model, update_step_counter, system_id) + plot_fun.plot_traj_from_ICS(np.array(conf.init_states_sim), TrOp, RLAC, update_step_counter=update_step_counter, ep=ep,steps=conf.NSTEPS, init=1) + + # Update arrays to store the reward history and its average + ep_reward_arr[ep_arr_idx:ep_arr_idx+len(tmp)] = ep_return + ep_arr_idx += len(tmp) + + for i in range(len(tmp)): + print("Episode {} ---> Return = {}".format(ep*len(tmp) + i, ep_return[i])) + + if update_step_counter > conf.NUPDATES: + break + + time_end = time.time() + print('Elapsed time: ', time_end-time_start) + + if conf.profile: + profiler.disable() + stats = pstats.Stats(profiler).sort_stats('cumtime') + stats.print_stats() + + # Plot returns + plot_fun.plot_Return(ep_reward_arr) + + # Save networks at the end of the training + RLAC.RL_save_weights() + + # Simulate the final policy + plot_fun.rollout(update_step_counter, RLAC.actor_model, conf.init_states_sim) diff --git a/Results Single Integrator/Results set test/Code/N_try_5/plot_utils.py b/Results Single Integrator/Results set test/Code/N_try_5/plot_utils.py new file mode 100644 index 0000000..55daab0 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/plot_utils.py @@ -0,0 +1,714 @@ +import math +import numpy as np +import tensorflow as tf +import matplotlib.pyplot as plt +from matplotlib import cm, colors +from matplotlib.patches import Ellipse, FancyBboxPatch, Rectangle +from matplotlib.transforms import Affine2D +import mpl_toolkits.mplot3d.art3d as art3d + +class PLOT(): + def __init__(self, N_try, env, NN, conf): + ''' + :input N_try : (Test number) + + :input env : (Environment instance) + + :input conf : (Configuration file) + :param fig_ax_lim : (float array) Figure axis limit [x_min, x_max, y_min, y_max] + :param Fig_path : (str) Figure path + :param NSTEPS : (int) Max episode length + :param nb_state : (int) State size (robot state size + 1) + :param nb_action : (int) Action size (robot action size) + :param NORMALIZE_INPUTS : (bool) Flag to normalize inputs (state) + :param state_norm_array : (float array) Array used to normalize states + :param dt : (float) Timestep + :param TARGET_STATE : (float array) Target position + :param cost_funct_param (float array) Cost function scale and offset factors + :param soft_max_param : (float array) Soft parameters array + :param obs_param : (float array) Obtacle parameters array + ''' + self.env = env + self.NN = NN + self.conf = conf + + self.N_try = N_try + + self.xlim = conf.fig_ax_lim[0].tolist() + self.ylim = conf.fig_ax_lim[1].tolist() + + # Set the ticklabel font size globally + plt.rcParams['xtick.labelsize'] = 22 + plt.rcParams['ytick.labelsize'] = 22 + plt.rcParams.update({'font.size': 20}) + + return + + def plot_obstaces(self, a=1): + if self.conf.system_id == 'car_park': + obs1 = Rectangle((self.conf.XC1-self.conf.A1/2, self.conf.YC1-self.conf.B1/2), self.conf.A1, self.conf.B1, 0.0,alpha=a) + obs1.set_facecolor([30/255, 130/255, 76/255, 1]) + obs2 = Rectangle((self.conf.XC2-self.conf.A2/2, self.conf.YC2-self.conf.B2/2), self.conf.A2, self.conf.B2, 0.0,alpha=a) + obs2.set_facecolor([30/255, 130/255, 76/255, 1]) + obs3 = Rectangle((self.conf.XC3-self.conf.A3/2, self.conf.YC3-self.conf.B3/2), self.conf.A3, self.conf.B3, 0.0,alpha=a) + obs3.set_facecolor([30/255, 130/255, 76/255, 1]) + + #rec1 = FancyBboxPatch((self.conf.XC1-self.conf.A1/2, self.conf.YC1-self.conf.B1/2), self.conf.A1, self.conf.B1,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec1.set_facecolor([30/255, 130/255, 76/255, 1]) + #rec2 = FancyBboxPatch((self.conf.XC2-self.conf.A2/2, self.conf.YC2-self.conf.B2/2), self.conf.A2, self.conf.B2,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec2.set_facecolor([30/255, 130/255, 76/255, 1]) + #rec3 = FancyBboxPatch((self.conf.XC3-self.conf.A3/2, self.conf.YC3-self.conf.B3/2), self.conf.A3, self.conf.B3,edgecolor='g', boxstyle='round,pad=0.1',alpha=a) + #rec3.set_facecolor([30/255, 130/255, 76/255, 1]) + else: + obs1 = Ellipse((self.conf.XC1, self.conf.YC1), self.conf.A1, self.conf.B1, 0.0,alpha=a) + obs1.set_facecolor([30/255, 130/255, 76/255, 1]) + obs2 = Ellipse((self.conf.XC2, self.conf.YC2), self.conf.A2, self.conf.B2, 0.0,alpha=a) + obs2.set_facecolor([30/255, 130/255, 76/255, 1]) + obs3 = Ellipse((self.conf.XC3, self.conf.YC3), self.conf.A3, self.conf.B3, 0.0,alpha=a) + obs3.set_facecolor([30/255, 130/255, 76/255, 1]) + + return [obs1, obs2, obs3] + + def plot_Reward(self, plot_obs=0): + x = np.arange(-15, 15, 0.1) + y = np.arange(-10, 10, 0.1) + theta = np.pi/2 + ICS = np.array([np.array([i,j,0]) for i in x for j in y]) + state = np.array([self.compute_ICS(np.array([i,j,0]), 'car')[0] for i in x for j in y]) # for k in theta] + state[:,2] = theta + r = [self.env.reward(self.conf.cost_weights_running, s) for s in state] + mi = min(r) + ma = max(r) + norm = colors.Normalize(vmin=mi,vmax=ma) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + pti = ax.scatter(ICS[:,0], ICS[:,1], norm=norm, c=r, cmap=cm.get_cmap('hot_r')) + plt.colorbar(pti) + + if plot_obs: + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + + # Center and check points of 'car_park' system + #check_points_WF_i = np.array([[math.cos(theta), -math.sin(theta)], [math.sin(theta), math.cos(theta)]]).dot(self.conf.check_points_BF[0,:]) + ICS[0,:2] + #ax.scatter(check_points_WF_i[0], check_points_WF_i[1], c='b') + #for i in range(1,len(self.conf.check_points_BF)): + # check_points_WF_i = np.array([[math.cos(theta), -math.sin(theta)], [math.sin(theta), math.cos(theta)]]).dot(self.conf.check_points_BF[i,:]) + ICS[0,:2] + # ax.scatter(check_points_WF_i[0], check_points_WF_i[1], c='r') + + ax.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5, legend='Goal position') + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.set_title('Plane') + #ax.legend() + ax.grid(True) + plt.show() + + def compute_ICS(self, p_ee, sys_id, theta=None, continue_flag=0): + if sys_id == 'manipulator': + radius = math.sqrt((p_ee[0]-self.conf.x_base)**2+(p_ee[1])**2) + if radius > 30: + continue_flag = 1 + return None, continue_flag + + phi = math.atan2(p_ee[1]-self.conf.y_base,(p_ee[0]-self.conf.x_base)) # SUM OF THE ANGLES FIXED + X3rd_joint = (p_ee[0]-self.conf.x_base) - self.conf.l* math.cos(phi) + Y3rd_joint = (p_ee[1]-self.conf.y_base) - self.conf.l* math.sin(phi) + + if abs(X3rd_joint) <= 1e-6 and abs(Y3rd_joint) <= 1e-6: + continue_flag = 1 + return None, continue_flag + + c2 = (X3rd_joint**2 + Y3rd_joint**2 -2*self.conf.l**2)/(2*self.conf.l**2) + + if p_ee[1] >= 0: + s2 = math.sqrt(1-c2**2) + else: + s2 = -math.sqrt(1-c2**2) + + s1 = ((self.conf.l + self.conf.l*c2)*Y3rd_joint - self.conf.l*s2*X3rd_joint)/(X3rd_joint**2 + Y3rd_joint**2) + c1 = ((self.conf.l + self.conf.l*c2)*X3rd_joint - self.conf.l*s2*Y3rd_joint)/(X3rd_joint**2 + Y3rd_joint**2) + ICS_q0 = math.atan2(s1,c1) + ICS_q1 = math.atan2(s2,c2) + ICS_q2 = phi-ICS_q0-ICS_q1 + + ICS = np.array([ICS_q0, ICS_q1, ICS_q2, 0.0, 0.0, 0.0, 0.0]) + + elif sys_id == 'car': + if theta == None: + theta = 0*np.random.uniform(-math.pi,math.pi) + ICS = np.array([p_ee[0], p_ee[1], theta, 0.0, 0.0, 0.0]) + + elif sys_id == 'car_park': + if theta == None: + #theta = 0*np.random.uniform(-math.pi,math.pi) + theta = np.pi/2 + ICS = np.array([p_ee[0], p_ee[1], theta, 0.0, 0.0, 0.0]) + + elif sys_id == 'double_integrator': + ICS = np.array([p_ee[0], p_ee[1], 0.0, 0.0, 0.0]) + + elif sys_id == 'single_integrator': + ICS = np.array([p_ee[0], p_ee[1], 0.0]) + + return ICS, continue_flag + + def plot_policy(self, tau, x, y, steps, n_updates, diff_loc=0): + ''' Plot policy rollout from a single initial state as well as state and control trajectories ''' + timesteps = self.self.conf.dt*np.arange(steps) + + fig = plt.figure(figsize=(12,8)) + plt.suptitle('POLICY: Discrete model, N try = {} N updates = {}'.format(self.N_try,n_updates), y=1) + + ax1 = fig.add_subplot(2, 2, 1) + ax1.plot(timesteps, x, 'ro', linewidth=1, markersize=1, legedn='x') + ax1.plot(timesteps, y, 'bo', linewidth=1, markersize=1, legend='y') + ax1.set_xlabel('Time [s]') + ax1.set_ylabel('[m]') + ax1.set_title('End-Effector Position') + ax1.legend() + ax1.grid(True) + + col = ['ro', 'bo', 'go'] + ax2 = fig.add_subplot(2, 2, self.conf.nb_action) + for i in range(self.conf.nb_action): + ax2.plot(timesteps, tau[:,i], col[i], linewidth=1, markersize=1,legend='tau{}'.format(i)) + ax2.set_xlabel('Time [s]') + ax2.set_title('Controls') + ax2.legend() + ax2.grid(True) + + ax3 = fig.add_subplot(1, 2, 2) + ax3.plot(x, y, 'ro', linewidth=1, markersize=1) + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax3.add_artist(obs_plot_list[i]) + ax3.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=10) + ax3.set_xlim(self.xlim) + ax3.set_ylim(self.ylim) + ax3.set_aspect('equal', 'box') + ax3.set_xlabel('X [m]') + ax3.set_ylabel('Y [m]') + ax3.set_title('Plane') + ax3.grid(True) + + fig.tight_layout() + + if diff_loc==0: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationSingleInit_{}_{}'.format(self.N_try,n_updates)) + else: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationMultiInit_{}_{}'.format(self.N_try,n_updates)) + + plt.clf() + plt.close(fig) + + def plot_policy_eval(self, p_list, n_updates, diff_loc=0, theta=0): + ''' Plot only policy rollouts from multiple initial states ''' + fig = plt.figure(figsize=(12,8)) + plt.suptitle('POLICY: Discrete model, N try = {} N updates = {}'.format(self.N_try,n_updates), y=1) + + ax = fig.add_subplot(1, 1, 1) + for idx in range(len(p_list)): + ax.plot(p_list[idx][:,0], p_list[idx][:,1], marker='o', linewidth=0.3, markersize=1) + ax.plot(p_list[idx][0,0],p_list[idx][0,1],'ko',markersize=5) + if self.conf.system_id == 'car_park': + theta = p_list[idx][-1,2] + fancybox = FancyBboxPatch((0 - self.conf.L/2, 0 - self.conf.W/2), self.conf.L, self.conf.W, edgecolor='none', alpha=0.5, boxstyle='round,pad=0') + fancybox.set_transform(Affine2D().rotate_deg(np.rad2deg(theta)).translate(p_list[idx][-1,0], p_list[idx][-1,1]) + ax.transData) + ax.add_patch(fancybox) + + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + + ax.plot(self.conf.TARGET_STATE[0],self.conf.TARGET_STATE[1],'b*',markersize=10) + + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.grid(True) + fig.tight_layout() + if diff_loc==0: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationSingleInit_{}_{}'.format(self.N_try,n_updates)) + else: + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/PolicyEvaluationMultiInit_{}_{}'.format(self.N_try,n_updates)) + + plt.clf() + plt.close(fig) + + def rollout(self,update_step_cntr, actor_model, init_states_sim, diff_loc=0): + ''' Plot rollout of the actor from some initial states. It generates the results and then calls plot_policy() and plot_policy_eval() ''' + #tau_all_sim = [] + p_ee_all_sim = [] + + returns = {} + + for k in range(len(init_states_sim)): + rollout_controls = np.zeros((self.conf.NSTEPS,self.conf.nb_action)) + rollout_states = np.zeros((self.conf.NSTEPS+1,self.conf.nb_state)) + rollout_p_ee = np.zeros((self.conf.NSTEPS+1,3)) + rollout_episodic_reward = 0 + + rollout_p_ee[0,:] = self.env.get_end_effector_position(init_states_sim[k]) + rollout_states[0,:] = np.copy(init_states_sim[k]) + + for i in range(self.conf.NSTEPS): + rollout_controls[i,:] = tf.squeeze(self.NN.eval(actor_model, np.array([rollout_states[i,:]]))).numpy() + rollout_states[i+1,:], rwrd_sim = self.env.step(self.conf.cost_weights_running, rollout_states[i,:],rollout_controls[i,:]) + rollout_p_ee[i+1,:] = self.env.get_end_effector_position(rollout_states[i+1,:]) + + rollout_p_ee[i+1,-1] = rollout_states[i+1,2] ### !!! ### + + rollout_episodic_reward += rwrd_sim + + if k==0: + print("N try = {}: Simulation Return @ N updates = {} ==> {}".format(self.N_try,update_step_cntr,rollout_episodic_reward)) + + p_ee_all_sim.append(rollout_p_ee) + + returns[init_states_sim[k][0],init_states_sim[k][1]] = rollout_episodic_reward + + self.plot_policy_eval(p_ee_all_sim,update_step_cntr, diff_loc=diff_loc) + + return returns + + def plot_results(self, tau, ee_pos_TO, ee_pos_RL, steps, to=0): + ''' Plot results from TO and episode to check consistency ''' + timesteps = self.conf.dt*np.arange(steps+1) + fig = plt.figure(figsize=(12,8)) + if to: + plt.suptitle('TO EXPLORATION: N try = {}'.format(self.N_try), y=1, fontsize=20) + else: + plt.suptitle('POLICY EXPLORATION: N try = {}'.format(self.N_try), y=1, fontsize=20) + + ax1 = fig.add_subplot(2, 2, 1) + ax1.plot(timesteps, ee_pos_TO[:,0], 'ro', linewidth=1, markersize=1,legend="x_TO") + ax1.plot(timesteps, ee_pos_TO[:,1], 'bo', linewidth=1, markersize=1,legend="y_TO") + ax1.plot(timesteps, ee_pos_RL[:,0], 'go', linewidth=1, markersize=1,legend="x_RL") + ax1.plot(timesteps, ee_pos_RL[:,1], 'ko', linewidth=1, markersize=1,legend="y_RL") + ax1.set_xlabel('Time [s]') + ax1.set_ylabel('[m]') + ax1.set_title('End-Effector Position') + ax1.set_xlim(0, timesteps[-1]) + ax1.legend() + ax1.grid(True) + + ax2 = fig.add_subplot(2, 2, 3) + col = ['ro', 'bo', 'go'] + for i in range(self.conf.nb_action): + ax2.plot(timesteps[:-1], tau[:,i], col[i], linewidth=1, markersize=1,legend='tau{}'.format(i)) + ax2.set_xlabel('Time [s]') + ax2.set_title('Controls') + ax2.legend() + ax2.grid(True) + + ax3 = fig.add_subplot(1, 2, 2) + ax3.plot(ee_pos_TO[:,0], ee_pos_TO[:,1], 'ro', linewidth=1, markersize=2,legend='TO') + ax3.plot(ee_pos_RL[:,0], ee_pos_RL[:,1], 'bo', linewidth=1, markersize=2,legend='RL') + ax3.plot([ee_pos_TO[0,0]],[ee_pos_TO[0,1]],'ro',markersize=5) + ax3.plot([ee_pos_RL[0,0]],[ee_pos_RL[0,1]],'bo',markersize=5) + obs_plot_list = self.plot_obstaces() + for i in range(len(obs_plot_list)): + ax3.add_artist(obs_plot_list[i]) + ax3.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax3.set_xlim(self.xlim) + ax3.set_ylim(self.ylim) + ax3.set_aspect('equal', 'box') + ax3.set_xlabel('X [m]') + ax3.set_ylabel('Y [m]') + ax3.set_title('Plane') + ax3.legend() + ax3.grid(True) + + fig.tight_layout() + #plt.show() + + def plot_Return(self, ep_reward_list): + ''' Plot returns (not so meaningful given that the initial state, so also the time horizon, of each episode is randomized) ''' + fig = plt.figure(figsize=(15,8)) + ax = fig.add_subplot(1, 1, 1) + ax.set_yscale('log') + ax.plot(ep_reward_list**2) + ax.set_xlabel("Episode") + ax.set_ylabel("Return") + ax.set_title("N_try = {}".format(self.N_try)) + ax.grid(True) + plt.savefig(self.conf.Fig_path+'/N_try_{}'.format(self.N_try)+'/EpReturn_{}'.format(self.N_try)) + plt.close() + + def plot_Critic_Value_function(self, critic_model, n_update, sys_id, name='V'): + ''' Plot Value function as learned by the critic ''' + if sys_id == 'manipulator': + N_discretization_x = 60 + 1 + N_discretization_y = 60 + 1 + + plot_data = np.zeros(N_discretization_y*N_discretization_x)*np.nan + ee_pos = np.zeros((N_discretization_y*N_discretization_x,3))*np.nan + + for k_x in range(N_discretization_x): + for k_y in range(N_discretization_y): + ICS = self.env.reset() + ICS[-1] = 0 + ee_pos[k_x*(N_discretization_y)+k_y,:] = self.env.get_end_effector_position(ICS) + plot_data[k_x*(N_discretization_y)+k_y] = self.NN.eval(critic_model, np.array([ICS])) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot() + plt.scatter(ee_pos[:,0], ee_pos[:,1], c=plot_data, cmap=cm.coolwarm, antialiased=False) + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + plt.colorbar() + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/{}_{}'.format(self.conf.Fig_path,self.N_try,name,int(n_update))) + plt.close() + + else: + N_discretization_x = 30 + 1 + N_discretization_y = 30 + 1 + + plot_data = np.zeros((N_discretization_y,N_discretization_x))*np.nan + + ee_x = np.linspace(-15, 15, N_discretization_x) + ee_y = np.linspace(-15, 15, N_discretization_y) + + for k_y in range(N_discretization_y): + for k_x in range(N_discretization_x): + p_ee = np.array([ee_x[k_x], ee_y[k_y], 0]) + ICS, continue_flag = self.compute_ICS(p_ee, sys_id, continue_flag=0) + if continue_flag: + continue + plot_data[k_x,k_y] = self.NN.eval(critic_model, np.array([ICS])) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot() + plt.contourf(ee_x, ee_y, plot_data.T, cmap=cm.coolwarm, antialiased=False) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + plt.colorbar() + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/{}_{}'.format(self.conf.Fig_path,self.N_try,name,int(n_update))) + plt.close() + + def plot_Critic_Value_function_from_sample(self, n_update, NSTEPS_SH, state_arr, reward_arr): + # Store transition after computing the (partial) cost-to go when using n-step TD (from 0 to Monte Carlo) + reward_to_go_arr = np.zeros(sum(NSTEPS_SH)+len(NSTEPS_SH)*1) + idx = 0 + for n in range(len(NSTEPS_SH)): + for i in range(NSTEPS_SH[n]+1): + # Compute the partial cost to go + reward_to_go_arr[idx] = sum(reward_arr[n][i:]) + idx += 1 + + state_arr = np.concatenate(state_arr, axis=0) + ee_pos_arr = np.zeros((len(state_arr),3)) + for i in range(state_arr.shape[0]): + ee_pos_arr[i,:] = self.env.get_end_effector_position(state_arr[i]) + + + mi = min(reward_to_go_arr) + ma = max(reward_to_go_arr) + + fig = plt.figure(figsize=(8,8)) + ax = fig.add_subplot()#projection='3d') + norm = colors.Normalize(vmin=mi,vmax=ma) + + obs_plot_list = self.plot_obstaces(a=0.5) + + ax.scatter(ee_pos_arr[:,0],ee_pos_arr[:,1], c=reward_to_go_arr, norm=norm, cmap=cm.coolwarm, marker='x') + + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + + plt.colorbar(cm.ScalarMappable(norm=norm,cmap=cm.coolwarm)) + plt.title('N_try {} - n_update {}'.format(self.N_try, n_update)) + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/V_sample_{}'.format(self.conf.Fig_path,self.N_try,int(n_update))) + plt.close() + + def plot_ICS(self,state_arr): + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + for j in range(len(state_arr)): + ax.scatter(state_arr[j][0,0],state_arr[j][0,1]) + obs_plot_list = plot_fun.plot_obstaces() + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.fig_ax_lim[0].tolist()) + ax.set_ylim(self.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + plt.savefig('{}/N_try_{}/ICS_{}_S{}'.format(conf.Fig_path,N_try,update_step_counter,int(w_S))) + plt.close(fig) + + def plot_rollout_and_traj_from_ICS(self, init_state, n_update, actor_model, TrOp, tag, steps=200): + ''' Plot results from TO and episode to check consistency ''' + colors = cm.coolwarm(np.linspace(0.1,1,len(init_state))) + + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + + for j in range(len(init_state)): + + ee_pos_TO = np.zeros((steps,3)) + ee_pos_RL = np.zeros((steps,3)) + + RL_states = np.zeros((steps,self.conf.nb_state)) + RL_action = np.zeros((steps-1,self.conf.nb_action)) + RL_states[0,:] = init_state[j,:] + ee_pos_RL[0,:] = self.env.get_end_effector_position(RL_states[0,:]) + + for i in range(steps-1): + RL_action[i,:] = self.NN.eval(actor_model, np.array([RL_states[i,:]])) + RL_states[i+1,:] = self.env.simulate(RL_states[i,:], RL_action[i,:]) + ee_pos_RL[i+1,:] = self.env.get_end_effector_position(RL_states[i+1,:]) + + TO_states, _ = TrOp.TO_System_Solve3(init_state[j,:], RL_states.T, RL_action.T, steps-1) + + try: + for i in range(steps): + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[i,:]) + except: + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[0,:]) + + ax.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax.scatter(ee_pos_TO[0,0],ee_pos_TO[0,1],color=colors[j]) + ax.scatter(ee_pos_RL[0,0],ee_pos_RL[0,1],color=colors[j]) + ax.plot(ee_pos_TO[1:,0],ee_pos_TO[1:,1],color=colors[j]) + ax.plot(ee_pos_RL[1:,0],ee_pos_RL[1:,1],'--',color=colors[j]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax.add_patch(obs_plot_list[i]) + + ax.set_xlim(self.xlim) + ax.set_ylim(self.ylim) + ax.set_aspect('equal', 'box') + ax.set_xlabel('X [m]') + ax.set_ylabel('Y [m]') + ax.set_title('Plane') + #ax.legend() + ax.grid(True) + + plt.savefig('{}/N_try_{}/ee_traj_{}_{}'.format(self.conf.Fig_path,self.N_try,int(n_update), tag)) + + def plot_ICS(self, input_arr, cs=0): + if cs == 1: + p_arr = np.zeros((len(input_arr),3)) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + for j in range(len(input_arr)): + p_arr[j,:] = input_arr[j,:] + ax.scatter(p_arr[:,0],p_arr[:,1]) + obs_plot_list = self.plot_obstaces(a = 0.5) + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.conf.fig_ax_lim[0].tolist()) + ax.set_ylim(self.conf.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + ax.grid() + plt.savefig('{}/N_try_{}/ICS'.format(self.conf.Fig_path,self.N_try)) + plt.close(fig) + else: + p_arr = np.zeros((len(input_arr),3)) + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot() + + for j in range(len(input_arr)): + p_arr[j,:] = self.env.get_end_effector_position(input_arr[j]) + ax.scatter(p_arr[:,0],p_arr[:,1]) + obs_plot_list = self.plot_obstaces(a = 0.5) + for i in range(len(obs_plot_list)): + ax.add_artist(obs_plot_list[i]) + ax.set_xlim(self.conf.fig_ax_lim[0].tolist()) + ax.set_ylim(self.conf.fig_ax_lim[1].tolist()) + ax.set_aspect('equal', 'box') + ax.grid() + plt.savefig('{}/N_try_{}/ICS'.format(self.conf.Fig_path,self.N_try)) + plt.close(fig) + + def plot_traj_from_ICS(self, init_state, TrOp, RLAC, update_step_counter=0,ep=0,steps=200, init=0,continue_flag=1): + ''' Plot results from TO and episode to check consistency ''' + colors = cm.coolwarm(np.linspace(0.1,1,len(init_state))) + + fig = plt.figure(figsize=(12,8)) + ax1 = fig.add_subplot(1,2,1) + ax2 = fig.add_subplot(1,2,2) + + for j in range(len(init_state)): + + ee_pos_TO = np.zeros((steps,3)) + ee_pos_RL = np.zeros((steps,3)) + + if init == 0: + # zeros + _, init_TO_states, init_TO_controls, _, success_init_flag = RLAC.create_TO_init(0, init_state[j,:]) + elif init == 1: + # NN + _, init_TO_states, init_TO_controls, _, success_init_flag = RLAC.create_TO_init(1, init_state[j,:]) + + if success_init_flag: + _, _, TO_states, _, _, _ = TrOp.TO_System_Solve(init_state[j,:], init_TO_states, init_TO_controls, steps-1) + else: + continue + + try: + for i in range(steps): + ee_pos_RL[i,:] = self.env.get_end_effector_position(init_TO_states[i,:]) + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[i,:]) + except: + ee_pos_RL[i,:] = self.env.get_end_effector_position(init_TO_states[0,:]) + ee_pos_TO[i,:] = self.env.get_end_effector_position(TO_states[0,:]) + + ax1.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax1.scatter(ee_pos_RL[0,0],ee_pos_RL[0,1],color=colors[j]) + ax1.plot(ee_pos_RL[1:,0],ee_pos_RL[1:,1],'--',color=colors[j]) + + ax2.plot([self.conf.TARGET_STATE[0]],[self.conf.TARGET_STATE[1]],'b*',markersize=5) + ax2.scatter(ee_pos_TO[0,0],ee_pos_TO[0,1],color=colors[j]) + ax2.plot(ee_pos_TO[1:,0],ee_pos_TO[1:,1],color=colors[j]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax1.add_patch(obs_plot_list[i]) + + obs_plot_list = self.plot_obstaces(a=0.5) + for i in range(len(obs_plot_list)): + ax2.add_patch(obs_plot_list[i]) + + ax1.set_xlim(self.xlim) + ax1.set_ylim(self.ylim) + ax1.set_aspect('equal', 'box') + ax1.set_xlabel('X [m]') + ax1.set_ylabel('Y [m]') + ax1.set_title('Warmstart traj.') + + ax2.set_xlim(self.xlim) + ax2.set_ylim(self.ylim) + ax2.set_aspect('equal', 'box') + ax2.set_xlabel('X [m]') + #ax2.set_ylabel('Y [m]') + ax2.set_title('TO traj.') + #ax.legend() + ax1.grid(True) + ax2.grid(True) + + plt.savefig('{}/N_try_{}/ee_traj_{}_{}'.format(self.conf.Fig_path,self.N_try,init,update_step_counter)) + + + + +if __name__ == '__main__': + import os + import sys + import time + import random + import importlib + import numpy as np + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # {'0' -> show all logs, '1' -> filter out info, '2' -> filter out warnings} + import tensorflow as tf + import matplotlib.pyplot as plt + import mpl_toolkits.mplot3d.art3d as art3d + + from RL import RL_AC + from plot_utils import PLOT + from NeuralNetwork import NN + + ### Input ### + N_try = 0 + + seed = 0 + tf.random.set_seed(seed) # Set tensorflow seed + random.seed(seed) # Set random seed + + system_id = 'car_park' + + TO_method = 'casadi' + + recover_training_flag = 0 + + CPU_flag = 0 + if CPU_flag: + os.environ["CUDA_VISIBLE_DEVICES"]="-1" + tf.config.experimental.list_physical_devices('GPU') + + nb_cpus = 1 + + w_S = 0 + ################################# + + # Import configuration file and environment file + system_map = { + 'single_integrator': ('conf_single_integrator', 'SingleIntegrator'), + 'double_integrator': ('conf_double_integrator', 'DoubleIntegrator'), + 'car': ('conf_car', 'Car'), + 'car_park': ('conf_car_park', 'CarPark'), + 'manipulator': ('conf_manipulator', 'Manipulator'), + 'ur5': ('conf_ur5', 'UR5') + } + + try: + conf_module, env_class = system_map[system_id] + conf = importlib.import_module(conf_module) + Environment = getattr(importlib.import_module('environment'), env_class) + except KeyError: + print('System {} not found'.format(system_id)) + sys.exit() + + + + # Create folders to store the results and the trained NNs and save configuration + for path in conf.path_list: + os.makedirs(path + '/N_try_{}'.format(N_try), exist_ok=True) + os.makedirs(conf.Config_path, exist_ok=True) + + params = [p for p in conf.__dict__ if not p.startswith("__")] + with open(conf.Config_path + '/config{}.txt'.format(N_try), 'w') as f: + for p in params: + f.write('{} = {}\n'.format(p, conf.__dict__[p])) + f.write('Seed = {}\n'.format(seed)) + f.write('w_S = {}'.format(w_S)) + + + + # Create environment instances + env = Environment(conf) + + # Create NN instance + NN_inst = NN(env, conf, w_S) + + # Create RL_AC instance + RLAC = RL_AC(env, NN_inst, conf, N_try) + + # Set initial weights of the NNs, initialize the counter of the updates and setup NN models + if recover_training_flag: + recover_training = np.array([conf.NNs_path_rec, conf.N_try_rec, conf.update_step_counter_rec]) + update_step_counter = conf.update_step_counter_rec + nb_starting_episode = (conf.update_step_counter_rec/conf.UPDATE_LOOPS)+1 + + RLAC.setup_model(recover_training) + else: + update_step_counter = 0 + nb_starting_episode = 0 + + RLAC.setup_model() + + # Create PLOT instance + plot_fun = PLOT(N_try, env, NN_inst, conf) + + plot_fun.plot_Reward() \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_5/replay_buffer.py b/Results Single Integrator/Results set test/Code/N_try_5/replay_buffer.py new file mode 100644 index 0000000..ba1804d --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/replay_buffer.py @@ -0,0 +1,240 @@ +import math +import random +import numpy as np +import tensorflow as tf +import segment_tree +#from stable_baselines.common.segment_tree import SumSegmentTree, MinSegmentTree + + +class ReplayBuffer(object): + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param BATCH_SIZE : (int) Size of the mini-batch + :param nb_state : (int) State size (robot state size + 1) + ''' + + self.conf = conf + self.storage_mat = np.zeros((conf.REPLAY_SIZE, conf.nb_state + 1 + conf.nb_state + conf.nb_state + 1 + 1)) + self.next_idx = 0 + self.full = 0 + self.exp_counter = np.zeros(conf.REPLAY_SIZE) + + def add(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Add transitions to the buffer ''' + data = self.concatenate_sample(obses_t, rewards, obses_t1, dVdxs, dones, terms) + + if len(data) + self.next_idx > self.conf.REPLAY_SIZE: + self.storage_mat[self.next_idx:,:] = data[:self.conf.REPLAY_SIZE-self.next_idx,:] + self.storage_mat[:self.next_idx+len(data)-self.conf.REPLAY_SIZE,:] = data[self.conf.REPLAY_SIZE-self.next_idx:,:] + self.full = 1 + else: + self.storage_mat[self.next_idx:self.next_idx+len(data),:] = data + + self.next_idx = (self.next_idx + len(data)) % self.conf.REPLAY_SIZE + + def sample(self): + ''' Sample a batch of transitions ''' + # Select indexes of the batch elements + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + idxes = np.random.randint(0, max_idx, size=self.conf.BATCH_SIZE) + + obses_t = self.storage_mat[idxes, :self.conf.nb_state] + rewards = self.storage_mat[idxes, self.conf.nb_state:self.conf.nb_state+1] + obses_t1 = self.storage_mat[idxes, self.conf.nb_state+1:self.conf.nb_state*2+1] + dVdxs = self.storage_mat[idxes, self.conf.nb_state*2+1:self.conf.nb_state*3+1] + dones = self.storage_mat[idxes, self.conf.nb_state*3+1:self.conf.nb_state*3+2] + terms = self.storage_mat[idxes, self.conf.nb_state*3+2:self.conf.nb_state*3+3] + + # Priorities not used + weights = np.ones((self.conf.BATCH_SIZE,1)) + batch_idxes = None + + # Convert the sample in tensor + obses_t, rewards, obses_t1, dVdxs, dones, weights = self.convert_sample_to_tensor(obses_t, rewards, obses_t1, dVdxs, dones, weights) + + return obses_t, rewards, obses_t1, dVdxs, dones, terms, weights, batch_idxes + + def concatenate_sample(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Convert batch of transitions into a tensor ''' + obses_t = np.concatenate(obses_t, axis=0) + rewards = np.concatenate(rewards, axis=0) + obses_t1 = np.concatenate(obses_t1, axis=0) + dVdxs = np.concatenate(dVdxs, axis=0) + dones = np.concatenate(dones, axis=0) + terms = np.concatenate(terms, axis=0) + + return np.concatenate((obses_t, rewards.reshape(-1,1), obses_t1, dVdxs, dones.reshape(-1,1), terms.reshape(-1,1)),axis=1) + + def convert_sample_to_tensor(self, obses_t, rewards, obses_t1, dVdxs, dones, weights): + ''' Convert batch of transitions into a tensor ''' + obses_t = tf.convert_to_tensor(obses_t, dtype=tf.float32) + rewards = tf.convert_to_tensor(rewards, dtype=tf.float32) + obses_t1 = tf.convert_to_tensor(obses_t1, dtype=tf.float32) + dVdxs = tf.convert_to_tensor(dVdxs, dtype=tf.float32) + dones = tf.convert_to_tensor(dones, dtype=tf.float32) + weights = tf.convert_to_tensor(weights, dtype=tf.float32) + + return obses_t, rewards, obses_t1, dVdxs, dones, weights + + + +class PrioritizedReplayBuffer: + def __init__(self, conf): + ''' + :input conf : (Configuration file) + + :param REPLAY_SIZE : (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped + :param BATCH_SIZE : (int) Size of the mini-batch + :param nb_state : (int) State size (robot state size + 1) + :param prioritized_replay_alpha : (float) Determines how much prioritization is used, set to 0 to use a normal buffer + :param prioritized_replay_beta : (float) Small positive constant that prevents the edge-case of transitions not being revisited once their error is zero + ''' + + self.conf = conf + + self.storage_mat = np.zeros((conf.REPLAY_SIZE, conf.nb_state + 1 + conf.nb_state + conf.nb_state + 1 + 1)) + self.full = 0 + self.next_idx = 0 + self.exp_counter = np.zeros(self.conf.REPLAY_SIZE) + self.priorities = np.empty(self.conf.REPLAY_SIZE) + + assert conf.prioritized_replay_alpha >= 0 + assert conf.prioritized_replay_beta > 0 + + it_capacity = 1 + while it_capacity < self.conf.REPLAY_SIZE: + it_capacity *= 2 + + self._it_sum = SumSegmentTree(it_capacity) + self._it_min = MinSegmentTree(it_capacity) + self._max_priority = 1.0 + + #self.RB_type = 'ReLO' + + self.MSE = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE) + + def add(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Add transitions to the buffer ''' + data = self.concatenate_sample(obses_t, rewards, obses_t1, dVdxs, dones, terms) + + if len(data) + self.next_idx > self.conf.REPLAY_SIZE: + self.storage_mat[self.next_idx:,:] = data[:self.conf.REPLAY_SIZE-self.next_idx,:] + self.storage_mat[:self.next_idx+len(data)-self.conf.REPLAY_SIZE,:] = data[self.conf.REPLAY_SIZE-self.next_idx:,:] + self.full = 1 + else: + self.storage_mat[self.next_idx:self.next_idx+len(data),:] = data + + for i in range(len(data)): + self._it_sum[(self.next_idx+i) % self.conf.REPLAY_SIZE] = self._max_priority ** self.conf.prioritized_replay_alpha + self._it_min[(self.next_idx+i) % self.conf.REPLAY_SIZE] = self._max_priority ** self.conf.prioritized_replay_alpha + + self.next_idx = (self.next_idx + len(data)) % self.conf.REPLAY_SIZE + + def _sample_proportional(self): + ''' Sample a batch of transitions ''' + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + + idx_arr = np.zeros(self.conf.BATCH_SIZE) + + p_total = self._it_sum.sum(0, max_idx - 1) + + segment = p_total / self.conf.BATCH_SIZE + + for i in range(self.conf.BATCH_SIZE): + p = random.random() * segment + i * segment #random.random(segment*i, segment*(i+1)) + idx = self._it_sum.find_prefixsum_idx(p) + idx_arr[i] = idx + + return idx_arr + + def sample(self): + ''' Sample a batch of transitions ''' + # Select indexes of the batch elements + if self.full: + max_idx = self.conf.REPLAY_SIZE + else: + max_idx = self.next_idx + + idxes = self._sample_proportional() + batch_idxes = idxes.astype(int) + + # Compute weights normalization + p_min = self._it_min.min() / self._it_sum.sum() + max_weight = (p_min * max_idx) ** (-self.conf.prioritized_replay_beta) + + self.exp_counter[batch_idxes] += 1 + self.priorities[batch_idxes] = self._it_sum[batch_idxes] / self._it_sum.sum() + weights = (self.priorities[batch_idxes] * max_idx) ** (-self.conf.prioritized_replay_beta) / max_weight + + obses_t = self.storage_mat[batch_idxes, :self.conf.nb_state] + rewards = self.storage_mat[batch_idxes, self.conf.nb_state:self.conf.nb_state+1] + obses_t1 = self.storage_mat[batch_idxes, self.conf.nb_state+1:self.conf.nb_state*2+1] + dVdxs = self.storage_mat[batch_idxes, self.conf.nb_state*2+1:self.conf.nb_state*3+1] + dones = self.storage_mat[batch_idxes, self.conf.nb_state*3+1:self.conf.nb_state*3+2] + terms = self.storage_mat[batch_idxes, self.conf.nb_state*3+2:self.conf.nb_state*3+3] + + # Convert the sample in tensor + obses_t, rewards, obses_t1, dVdxs, dones, weights = self.convert_sample_to_tensor(obses_t, rewards, obses_t1, dVdxs, dones, weights) + + return obses_t, rewards, obses_t1, dVdxs, dones, terms, weights, batch_idxes + + def update_priorities(self, idxes, reward_to_go_batch, critic_value, target_critic_value=None): + '''Update priorities of sampled transitions ''' + # Create td_errors + if self.RB_type == 'ReLO': + # MSE(Delta V_c) - MSE(Delta V_t) + td_errors = self.MSE(reward_to_go_batch, critic_value).numpy()-self.MSE(reward_to_go_batch, target_critic_value).numpy() + td_errors_norm = np.clip(td_errors, 0, np.max(td_errors)) + else: # 'PER' is default self.RB_type + # |TD_error_i| + td_errors_norm = tf.math.abs(tf.math.subtract(reward_to_go_batch, critic_value)) + td_errors_norm = td_errors_norm[:,0] + + # Compute the freshness discount factor + fresh_disc_factor = self.conf.fresh_factor**self.exp_counter[idxes] + + # Compute new priorities: p_i = mu**C_i * td_error + self.conf.prioritized_replay_eps + new_priorities = fresh_disc_factor * td_errors_norm + self.conf.prioritized_replay_eps + + # Sets priority of transition at index idxes[i] in buffer to priorities[i] + assert len(idxes) == len(new_priorities) + for idx, priority in zip(idxes, new_priorities): + assert priority > 0 + + idx = int(idx) + + self._it_sum[idx] = priority ** self.conf.prioritized_replay_alpha + self._it_min[idx] = priority ** self.conf.prioritized_replay_alpha + + self._max_priority = max(self._max_priority, priority) + + def concatenate_sample(self, obses_t, rewards, obses_t1, dVdxs, dones, terms): + ''' Convert batch of transitions into a tensor ''' + obses_t = np.concatenate(obses_t, axis=0) + rewards = np.concatenate(rewards, axis=0) + obses_t1 = np.concatenate(obses_t1, axis=0) + dVdxs = np.concatenate(dVdxs, axis=0) + dones = np.concatenate(dones, axis=0) + terms = np.concatenate(terms, axis=0) + + return np.concatenate((obses_t, rewards.reshape(-1,1), obses_t1, dVdxs, dones.reshape(-1,1), terms.reshape(-1,1)),axis=1) + + def convert_sample_to_tensor(self, obses_t, rewards, obses_t1, dVdxs, dones, weights): + ''' Convert batch of transitions into a tensor ''' + obses_t = tf.convert_to_tensor(obses_t, dtype=tf.float32) + rewards = tf.convert_to_tensor(rewards, dtype=tf.float32) + obses_t1 = tf.convert_to_tensor(obses_t1, dtype=tf.float32) + dVdxs = tf.convert_to_tensor(dVdxs, dtype=tf.float32) + dones = tf.convert_to_tensor(dones, dtype=tf.float32) + weights = tf.convert_to_tensor(weights, dtype=tf.float32) + + return obses_t, rewards, obses_t1, dVdxs, dones, weights diff --git a/Results Single Integrator/Results set test/Code/N_try_5/robot_utils.py b/Results Single Integrator/Results set test/Code/N_try_5/robot_utils.py new file mode 100644 index 0000000..815ff31 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/robot_utils.py @@ -0,0 +1,637 @@ +import os +import math +import random +import gepetto#.corbaserver +import time +import subprocess +import numpy as np +import pinocchio as pin +from numpy.linalg import norm +from pinocchio.robot_wrapper import RobotWrapper as PinocchioRobotWrapper + +class Contact: + ''' A contact between a contact-point and a contact-surface + ''' + def __init__(self, contact_point, contact_surface): + self.cp = contact_point + self.cs = contact_surface + self.reset_contact_position() + + def reset_contact_position(self): + # Initialize anchor point p0, that is the initial (0-load) position of the spring + self.p0 = self.cp.get_position() + self.in_contact = True + + def compute_force(self): + self.f, self.p0 = self.cs.compute_force(self.cp, self.p0) + return self.f + + def get_jacobian(self): + return self.cp.get_jacobian() + +class RobotWrapper(PinocchioRobotWrapper): + + @staticmethod + def BuildFromURDF(filename, package_dirs=None, root_joint=None, verbose=False, meshLoader=None): + robot = RobotWrapper() + robot.initFromURDF(filename, package_dirs, root_joint, verbose, meshLoader) + return robot + + @property + def na(self): + if(self.model.joints[0].nq==7): + return self.model.nv-6 + return self.model.nv + + def mass(self, q, update=True): + if(update): + return pin.crba(self.model, self.data, q) + return self.data.M + + def nle(self, q, v, update=True): + if(update): + return pin.nonLinearEffects(self.model, self.data, q, v) + return self.data.nle + + def com(self, q=None, v=None, a=None, update=True): + if(update==False or q is None): + return PinocchioRobotWrapper.com(self, q) + if a is None: + if v is None: + return PinocchioRobotWrapper.com(self, q) + return PinocchioRobotWrapper.com(self, q, v) + return PinocchioRobotWrapper.com(self, q, v,a) + + def Jcom(self, q, update=True): + if(update): + return pin.jacobianCenterOfMass(self.model, self.data, q) + return self.data.Jcom + + def momentumJacobian(self, q, v, update=True): + if(update): + pin.ccrba(self.model, self.data, q, v) + return self.data.Ag + + def computeAllTerms(self, q, v): + ''' pin.computeAllTerms is equivalent to calling: + pinocchio::forwardKinematics + pinocchio::crba + pinocchio::nonLinearEffects + pinocchio::computeJointJacobians + pinocchio::centerOfMass + pinocchio::jacobianCenterOfMass + pinocchio::kineticEnergy + pinocchio::potentialEnergy + This is too much for our needs, so we call only the functions + we need, including those for the frame kinematics + ''' + #pin.computeAllTerms(self.model, self.data, q, v); + pin.forwardKinematics(self.model, self.data, q, v, np.zeros(self.model.nv)) + pin.computeJointJacobians(self.model, self.data) + pin.updateFramePlacements(self.model, self.data) + pin.crba(self.model, self.data, q) + pin.nonLinearEffects(self.model, self.data, q, v) + + def forwardKinematics(self, q, v=None, a=None): + if v is not None: + if a is not None: + pin.forwardKinematics(self.model, self.data, q, v, a) + else: + pin.forwardKinematics(self.model, self.data, q, v) + else: + pin.forwardKinematics(self.model, self.data, q) + + def inverseKinematics(self, ee_coords): + ''' Inverse kinematics algorithm to compute a joint configuration given the EE coordinates''' + + oMdes = pin.SE3(np.eye(3), np.array([ee_coords[0], ee_coords[1], 0.0])) + random.seed(123) + for j in range(100): + q = np.array([random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi)]) + # q = pin.neutral(self.model) + + pin.forwardKinematics(self.model, self.data, q) + pin.updateFramePlacements(self.model, self.data) + eps = 1e-4 + IT_MAX = 1000 + DT = 1e-1 + damp = 1e-12 + frame_id = self.model.getFrameId('fixed_ee') + i=0 + while True: + pin.forwardKinematics(self.model,self.data,q) + pin.updateFramePlacements(self.model, self.data) + dMi = oMdes.actInv(self.data.oMf[frame_id]) + err = pin.log(dMi).vector + if norm(err) < eps: + success = True + break + if i >= IT_MAX: + success = False + break + J = pin.computeFrameJacobian(self.model,self.data,q,frame_id) + v = - J.T.dot(np.linalg.solve(J.dot(J.T) + damp * np.eye(6), err)) + q = pin.integrate(self.model,q,v*DT) + if not i % 10: + print('%d: error = %s' % (i, err.T)) + i += 1 + + if success: + print("Convergence achieved!") + break + else: + print("\nWarning: the iterative algorithm has not reached convergence to the desired precision. Retry ({}) with another initial configuration".format(j)) + + return q, success + + def frameJacobian(self, q, index, update=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + ''' Call computeFrameJacobian if update is true. If not, user should call computeFrameJacobian first. + Then call getFrameJacobian and return the Jacobian matrix. + ref_frame can be: ReferenceFrame.LOCAL, ReferenceFrame.WORLD, ReferenceFrame.LOCAL_WORLD_ALIGNED + ''' + if(update): + pin.computeFrameJacobian(self.model, self.data, q, index) + return pin.getFrameJacobian(self.model, self.data, index, ref_frame) + + def frameVelocity(self, q, v, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v) + v_local = pin.getFrameVelocity(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return v_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + v_world = H.act(v_local) + return v_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + v = Hr.act(v_local) + return v + + def frameAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def frameClassicAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + v = pin.getFrameVelocity(self.model, self.data, index) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + a_local.linear += np.cross(v.angular, v.linear, axis=0) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def deactivateCollisionPairs(self, collision_pair_indexes): + for i in collision_pair_indexes: + self.collision_data.deactivateCollisionPair(i) + + def addAllCollisionPairs(self): + self.collision_model.addAllCollisionPairs() + self.collision_data = pin.GeometryData(self.collision_model) + + def isInCollision(self, q, stop_at_first_collision=True): + return pin.computeCollisions(self.model, self.data, self.collision_model, self.collision_data, np.asmatrix(q).reshape((self.model.nq,1)), stop_at_first_collision) + + def findFirstCollisionPair(self, consider_only_active_collision_pairs=True): + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + return (i, self.collision_model.collisionPairs[i]) + return None + + def findAllCollisionPairs(self, consider_only_active_collision_pairs=True): + res = [] + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + res += [(i, self.collision_model.collisionPairs[i])] + return res + +class RobotSimulator: + + def __init__(self, robot, q0_init, v0_init, simulation_type, tau_coulomb_max, use_viewer=False, DISPLAY_T=None, CAMERA_TRANSFORM=None, show_floor=False): + self.robot = robot + self.model = self.robot.model + self.data = self.model.createData() + self.t = 0.0 # time + self.nv = nv = self.model.nv # Dimension of joint velocities vector + self.na = na = robot.na # number of actuated joints + self.S = np.hstack((np.zeros((na, nv-na)), np.eye(na, na))) # Matrix S used as filter of vector of inputs U + + self.contacts = [] + self.candidate_contact_points = [] # candidate contact points + self.contact_surfaces = [] + + self.DISPLAY_T = DISPLAY_T # refresh period for viewer + self.use_viewer = use_viewer + self.tau_coulomb_max = tau_coulomb_max + self.display_counter = self.DISPLAY_T + self.init(q0_init, v0_init, True) + + self.tau_c = np.zeros(na) # Coulomb friction torque + self.simulation_type = simulation_type + self.set_coulomb_friction(tau_coulomb_max) + + # for gepetto viewer + self.gui = None + if(use_viewer): + try: + prompt = subprocess.getstatusoutput("ps aux |grep 'gepetto-gui'|grep -v 'grep'|wc -l") + if int(prompt[1]) == 0: + os.system('gepetto-gui &') + time.sleep(1) + except: + pass + gepetto.corbaserver.Client() + gepetto.Client() + self.robot.initViewer(loadModel=True) + self.gui = self.robot.viewer.gui + if(show_floor): + self.robot.viewer.gui.createSceneWithFloor('world') + self.gui.setLightingMode('world/floor', 'OFF') + self.robot.displayCollisions(False) + self.robot.displayVisuals(True) + self.robot.display(self.q) + try: + self.gui.setCameraTransform("python-pinocchio", CAMERA_TRANSFORM) + except: + self.gui.setCameraTransform(0, CAMERA_TRANSFORM) + + # Re-initialize the simulator + def init(self, q0=None, v0=None, reset_contact_positions=False): + self.first_iter = True + + if q0 is not None: + self.q = q0.copy() + + if(v0 is None): + self.v = np.zeros(self.robot.nv) + else: + self.v = v0.copy() + self.dv = np.zeros(self.robot.nv) + self.resize_contact_data(reset_contact_positions) + + def resize_contact_data(self, reset_contact_positions=False): + self.nc = len(self.contacts) # number of contacts + self.nk = 3*self.nc # size of contact force vector + self.f = np.zeros(self.nk) # contact forces + self.Jc = np.zeros((self.nk, self.model.nv)) # contact Jacobian + + # reset contact position + if(reset_contact_positions): + pin.forwardKinematics(self.model, self.data, self.q) + pin.updateFramePlacements(self.model, self.data) + for c in self.contacts: + c.reset_contact_position() + + self.compute_forces(compute_data=True) + + def set_coulomb_friction(self, tau_max): + self.tau_coulomb_max = 1e-2*tau_max*self.model.effortLimit + self.simulate_coulomb_friction = (norm(self.tau_coulomb_max)!=0.0) + + def collision_detection(self): + for s in self.contact_surfaces: # for each contact surface + for cp in self.candidate_contact_points: # for each candidate contact point + p = cp.get_position() + if(s.check_collision(p)): # check whether the point is colliding with the surface + if(not cp.active): # if the contact was not already active + print("Collision detected between point", cp.frame_name, " at ", p) + cp.active = True + cp.contact = Contact(cp, s) + self.contacts += [cp.contact] + self.resize_contact_data() + else: + if(cp.active): + print("Contact lost between point", cp.frame_name, " at ", p) + cp.active = False + self.contacts.remove(cp.contact) + self.resize_contact_data() + + def compute_forces(self, compute_data=True): + '''Compute the contact forces from q, v and elastic model''' + if compute_data: + pin.forwardKinematics(self.model, self.data, self.q, self.v) + # Computes the placements of all the operational frames according to the current joint placement stored in data + pin.updateFramePlacements(self.model, self.data) + self.collision_detection() + + i = 0 + for c in self.contacts: + self.f[i:i+3] = c.compute_force() + self.Jc[i:i+3, :] = c.get_jacobian() + i += 3 + return self.f + + def step(self, u, dt=None): + if dt is None: + dt = self.dt + + # (Forces are directly in the world frame, and aba wants them in the end effector frame) + pin.computeAllTerms(self.model, self.data, self.q, self.v) + pin.updateFramePlacements(self.model, self.data) + M = self.data.M + h = self.data.nle + self.collision_detection() + self.compute_forces(False) + + if(self.simulate_coulomb_friction and self.simulation_type=='timestepping'): + # minimize kinetic energy using time stepping + from quadprog import solve_qp + ''' + Solve a strictly convex quadratic program + + Minimize 1/2 x^T G x - a^T x + Subject to C.T x >= b + + Input Parameters + ---------- + G : array, shape=(n, n) + a : array, shape=(n,) + C : array, shape=(n, m) matrix defining the constraints + b : array, shape=(m), default=None, vector defining the constraints + meq : int, default=0 + the first meq constraints are treated as equality constraints, + all further as inequality constraints + ''' + # M (v' - v) = dt*S^T*(tau - tau_c) - dt*h + dt*J^T*f + # M v' = M*v + dt*(S^T*tau - h + J^T*f) - dt*S^T*tau_c + # M v' = b + B*tau_c + # v' = Minv*(b + B*tau_c) + b = M.dot(self.v) + dt*(self.S.T.dot(u) - h + self.Jc.T.dot(self.f)) + B = - dt*self.S.T + # Minimize kinetic energy: + # min v'.T * M * v' + # min (b+B*tau_c​).T*Minv*(b+B*tau_c​) + # min tau_c.T * B.T*Minv*B* tau_C + 2*b.T*Minv*B*tau_c + Minv = np.linalg.inv(M) + G = B.T.dot(Minv.dot(B)) + a = -b.T.dot(Minv.dot(B)) + C = np.vstack((np.eye(self.na), -np.eye(self.na))) + c = np.concatenate((-self.tau_coulomb_max, -self.tau_coulomb_max)) + solution = solve_qp(G, a, C.T, c, 0) + self.tau_c = solution[0] + self.v = Minv.dot(b + B.dot(self.tau_c)) + self.q = pin.integrate(self.model, self.q, self.v*dt) + self.dv = np.linalg.solve(M, self.S.T.dot(u-self.tau_c) - h + self.Jc.T.dot(self.f)) + elif(self.simulation_type=='euler' or self.simulate_coulomb_friction==False): + self.tau_c = self.tau_coulomb_max*np.sign(self.v[-self.na:]) + self.dv = np.linalg.solve(M, self.S.T.dot(u-self.tau_c) - h + self.Jc.T.dot(self.f)) + # v_mean = np.copy(self.v) + 0.5*dt*self.dv + self.q = pin.integrate(self.model, self.q, self.v*dt) + # self.q += dt*v_mean + self.v += self.dv*dt + else: + print("[ERROR] Unknown simulation type:", self.simulation_type) + + self.t += dt + return self.q, self.v, self.dv + + def reset(self): + self.first_iter = True + + def simulate(self, state, u, dt=0.05, ndt=1): + ''' Perform ndt steps, each lasting dt/ndt seconds ''' + self.q = state[:self.robot.nq] + self.v = state[self.robot.nq:self.robot.nv+self.robot.nq] + + tau_c_avg = 0*self.tau_c + for i in range(ndt): + self.q, self.v, self.dv = self.step(u, dt/ndt) + tau_c_avg += self.tau_c + self.tau_c = tau_c_avg / ndt + + if (self.use_viewer): + self.display_counter -= dt + if self.display_counter <= 0.0: + self.robot.display(self.q) + self.display_counter = self.DISPLAY_T + + return self.q, self.v, self.f + + def display(self, q): + if(self.use_viewer): + self.robot.display(q) + + +class RobotWrapper(PinocchioRobotWrapper): + + @staticmethod + def BuildFromURDF(filename, package_dirs=None, root_joint=None, verbose=False, meshLoader=None): + robot = RobotWrapper() + robot.initFromURDF(filename, package_dirs, root_joint, verbose, meshLoader) + return robot + + @property + def na(self): + if(self.model.joints[0].nq==7): + return self.model.nv-6 + return self.model.nv + + def mass(self, q, update=True): + if(update): + return pin.crba(self.model, self.data, q) + return self.data.M + + def nle(self, q, v, update=True): + if(update): + return pin.nonLinearEffects(self.model, self.data, q, v) + return self.data.nle + + def com(self, q=None, v=None, a=None, update=True): + if(update==False or q is None): + return PinocchioRobotWrapper.com(self, q) + if a is None: + if v is None: + return PinocchioRobotWrapper.com(self, q) + return PinocchioRobotWrapper.com(self, q, v) + return PinocchioRobotWrapper.com(self, q, v,a) + + def Jcom(self, q, update=True): + if(update): + return pin.jacobianCenterOfMass(self.model, self.data, q) + return self.data.Jcom + + def momentumJacobian(self, q, v, update=True): + if(update): + pin.ccrba(self.model, self.data, q, v) + return self.data.Ag + + def computeAllTerms(self, q, v): + ''' pin.computeAllTerms is equivalent to calling: + pinocchio::forwardKinematics + pinocchio::crba + pinocchio::nonLinearEffects + pinocchio::computeJointJacobians + pinocchio::centerOfMass + pinocchio::jacobianCenterOfMass + pinocchio::kineticEnergy + pinocchio::potentialEnergy + This is too much for our needs, so we call only the functions + we need, including those for the frame kinematics + ''' + #pin.computeAllTerms(self.model, self.data, q, v); + pin.forwardKinematics(self.model, self.data, q, v, np.zeros(self.model.nv)) + pin.computeJointJacobians(self.model, self.data) + pin.updateFramePlacements(self.model, self.data) + pin.crba(self.model, self.data, q) + pin.nonLinearEffects(self.model, self.data, q, v) + + def forwardKinematics(self, q, v=None, a=None): + if v is not None: + if a is not None: + pin.forwardKinematics(self.model, self.data, q, v, a) + else: + pin.forwardKinematics(self.model, self.data, q, v) + else: + pin.forwardKinematics(self.model, self.data, q) + + def inverseKinematics(self, ee_coords): + ''' Inverse kinematics algorithm to compute a joint configuration given the EE coordinates''' + + oMdes = pin.SE3(np.eye(3), np.array([ee_coords[0], ee_coords[1], 0.0])) + random.seed(123) + for j in range(100): + q = np.array([random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi),random.uniform(-math.pi,math.pi)]) + # q = pin.neutral(self.model) + + pin.forwardKinematics(self.model, self.data, q) + pin.updateFramePlacements(self.model, self.data) + eps = 1e-4 + IT_MAX = 1000 + DT = 1e-1 + damp = 1e-12 + frame_id = self.model.getFrameId('fixed_ee') + i=0 + while True: + pin.forwardKinematics(self.model,self.data,q) + pin.updateFramePlacements(self.model, self.data) + dMi = oMdes.actInv(self.data.oMf[frame_id]) + err = pin.log(dMi).vector + if norm(err) < eps: + success = True + break + if i >= IT_MAX: + success = False + break + J = pin.computeFrameJacobian(self.model,self.data,q,frame_id) + v = - J.T.dot(np.linalg.solve(J.dot(J.T) + damp * np.eye(6), err)) + q = pin.integrate(self.model,q,v*DT) + if not i % 10: + print('%d: error = %s' % (i, err.T)) + i += 1 + + if success: + print("Convergence achieved!") + break + else: + print("\nWarning: the iterative algorithm has not reached convergence to the desired precision. Retry ({}) with another initial configuration".format(j)) + + return q, success + + def frameJacobian(self, q, index, update=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + ''' Call computeFrameJacobian if update is true. If not, user should call computeFrameJacobian first. + Then call getFrameJacobian and return the Jacobian matrix. + ref_frame can be: ReferenceFrame.LOCAL, ReferenceFrame.WORLD, ReferenceFrame.LOCAL_WORLD_ALIGNED + ''' + if(update): + pin.computeFrameJacobian(self.model, self.data, q, index) + return pin.getFrameJacobian(self.model, self.data, index, ref_frame) + + def frameVelocity(self, q, v, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v) + v_local = pin.getFrameVelocity(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return v_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + v_world = H.act(v_local) + return v_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + v = Hr.act(v_local) + return v + + def frameAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def frameClassicAcceleration(self, q, v, a, index, update_kinematics=True, ref_frame=pin.ReferenceFrame.LOCAL_WORLD_ALIGNED): + if update_kinematics: + pin.forwardKinematics(self.model, self.data, q, v, a) + v = pin.getFrameVelocity(self.model, self.data, index) + a_local = pin.getFrameAcceleration(self.model, self.data, index) + a_local.linear += np.cross(v.angular, v.linear, axis=0) + if ref_frame==pin.ReferenceFrame.LOCAL: + return a_local + + H = self.data.oMf[index] + if ref_frame==pin.ReferenceFrame.WORLD: + a_world = H.act(a_local) + return a_world + + Hr = pin.pin(H.rotation, np.zeros(3)) + a = Hr.act(a_local) + return a + + def deactivateCollisionPairs(self, collision_pair_indexes): + for i in collision_pair_indexes: + self.collision_data.deactivateCollisionPair(i) + + def addAllCollisionPairs(self): + self.collision_model.addAllCollisionPairs() + self.collision_data = pin.GeometryData(self.collision_model) + + def isInCollision(self, q, stop_at_first_collision=True): + return pin.computeCollisions(self.model, self.data, self.collision_model, self.collision_data, np.asmatrix(q).reshape((self.model.nq,1)), stop_at_first_collision) + + def findFirstCollisionPair(self, consider_only_active_collision_pairs=True): + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + return (i, self.collision_model.collisionPairs[i]) + return None + + def findAllCollisionPairs(self, consider_only_active_collision_pairs=True): + res = [] + for i in range(len(self.collision_model.collisionPairs)): + if(not consider_only_active_collision_pairs or self.collision_data.activeCollisionPairs[i]): + if(pin.computeCollision(self.collision_model, self.collision_data, i)): + res += [(i, self.collision_model.collisionPairs[i])] + return res \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Code/N_try_5/segment_tree.py b/Results Single Integrator/Results set test/Code/N_try_5/segment_tree.py new file mode 100644 index 0000000..cb386ec --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/segment_tree.py @@ -0,0 +1,145 @@ +import operator + + +class SegmentTree(object): + def __init__(self, capacity, operation, neutral_element): + """Build a Segment Tree data structure. + + https://en.wikipedia.org/wiki/Segment_tree + + Can be used as regular array, but with two + important differences: + + a) setting item's value is slightly slower. + It is O(lg capacity) instead of O(1). + b) user has access to an efficient ( O(log segment size) ) + `reduce` operation which reduces `operation` over + a contiguous subsequence of items in the array. + + Paramters + --------- + capacity: int + Total size of the array - must be a power of two. + operation: lambda obj, obj -> obj + and operation for combining elements (eg. sum, max) + must form a mathematical group together with the set of + possible values for array elements (i.e. be associative) + neutral_element: obj + neutral element for the operation above. eg. float('-inf') + for max and 0 for sum. + """ + assert capacity > 0 and capacity & (capacity - 1) == 0, "capacity must be positive and a power of 2." + self._capacity = capacity + self._value = [neutral_element for _ in range(2 * capacity)] + self._operation = operation + + def _reduce_helper(self, start, end, node, node_start, node_end): + if start == node_start and end == node_end: + return self._value[node] + mid = (node_start + node_end) // 2 + if end <= mid: + return self._reduce_helper(start, end, 2 * node, node_start, mid) + else: + if mid + 1 <= start: + return self._reduce_helper(start, end, 2 * node + 1, mid + 1, node_end) + else: + return self._operation( + self._reduce_helper(start, mid, 2 * node, node_start, mid), + self._reduce_helper(mid + 1, end, 2 * node + 1, mid + 1, node_end) + ) + + def reduce(self, start=0, end=None): + """Returns result of applying `self.operation` + to a contiguous subsequence of the array. + + self.operation(arr[start], operation(arr[start+1], operation(... arr[end]))) + + Parameters + ---------- + start: int + beginning of the subsequence + end: int + end of the subsequences + + Returns + ------- + reduced: obj + result of reducing self.operation over the specified range of array elements. + """ + if end is None: + end = self._capacity + if end < 0: + end += self._capacity + end -= 1 + return self._reduce_helper(start, end, 1, 0, self._capacity - 1) + + def __setitem__(self, idx, val): + # index of the leaf + idx += self._capacity + self._value[idx] = val + idx //= 2 + while idx >= 1: + self._value[idx] = self._operation( + self._value[2 * idx], + self._value[2 * idx + 1] + ) + idx //= 2 + + def __getitem__(self, idx): + assert 0 <= idx < self._capacity + return self._value[self._capacity + idx] + + +class SumSegmentTree(SegmentTree): + def __init__(self, capacity): + super(SumSegmentTree, self).__init__( + capacity=capacity, + operation=operator.add, + neutral_element=0.0 + ) + + def sum(self, start=0, end=None): + """Returns arr[start] + ... + arr[end]""" + return super(SumSegmentTree, self).reduce(start, end) + + def find_prefixsum_idx(self, prefixsum): + """Find the highest index `i` in the array such that + sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum + + if array values are probabilities, this function + allows to sample indexes according to the discrete + probability efficiently. + + Parameters + ---------- + perfixsum: float + upperbound on the sum of array prefix + + Returns + ------- + idx: int + highest index satisfying the prefixsum constraint + """ + assert 0 <= prefixsum <= self.sum() + 1e-5 + idx = 1 + while idx < self._capacity: # while non-leaf + if self._value[2 * idx] > prefixsum: + idx = 2 * idx + else: + prefixsum -= self._value[2 * idx] + idx = 2 * idx + 1 + return idx - self._capacity + + +class MinSegmentTree(SegmentTree): + def __init__(self, capacity): + super(MinSegmentTree, self).__init__( + capacity=capacity, + operation=min, + neutral_element=float('inf') + ) + + def min(self, start=0, end=None): + """Returns min(arr[start], ..., arr[end])""" + + return super(MinSegmentTree, self).reduce(start, end) diff --git a/Results Single Integrator/Results set test/Code/N_try_5/utils.py b/Results Single Integrator/Results set test/Code/N_try_5/utils.py new file mode 100644 index 0000000..c426b89 --- /dev/null +++ b/Results Single Integrator/Results set test/Code/N_try_5/utils.py @@ -0,0 +1,42 @@ +import tensorflow as tf +import numpy as np + +def array2tensor(array): + + return tf.expand_dims(tf.convert_to_tensor(array), 0) + +def de_normalize_tensor(state, state_norm_arr): + ''' Retrieve state from normalized state - tensor ''' + state_time = tf.concat([tf.zeros([state.shape[0], state.shape[1]-1]), tf.reshape((state[:,-1]+1)*state_norm_arr[-1]/2,[state.shape[0],1])],1) + state_no_time = state * state_norm_arr + mask = tf.concat([tf.ones([state.shape[0], state.shape[1]-1]), tf.zeros([state.shape[0], 1])],1) + state_not_norm = state_no_time * mask + state_time * (1 - mask) + + return state_not_norm + +def normalize_tensor(state, state_norm_arr): + ''' Retrieve state from normalized state - tensor ''' + state_norm_time = tf.concat([tf.zeros([state.shape[0], state.shape[1]-1]), tf.reshape(((state[:,-1]) / state_norm_arr[-1])*2 - 1,[state.shape[0],1])],1) + state_norm_no_time = state / state_norm_arr + mask = tf.concat([tf.ones([state.shape[0], state.shape[1]-1]), tf.zeros([state.shape[0], 1])],1) + state_norm = state_norm_no_time * mask + state_norm_time * (1 - mask) + + return state_norm + +def de_normalize(state, state_norm_arr): + ''' Retrieve state from normalized state ''' + state_not_norm = np.empty_like(state) + state_not_norm[:-1] = state[:-1] * state_norm_arr[:-1] + state_not_norm[-1] = (state[-1] + 1) * state_norm_arr[-1]/2 + + return state_not_norm + +def normalize(state, state_norm_arr): + ''' Normalize state ''' + state_norm = np.empty_like(state) + state_norm = state / state_norm_arr + state_norm[-1] = state_norm[-1] * 2 -1 + + return state_norm + + diff --git a/Results Single Integrator/Results set test/Configs/conf_single_integrator_0.py b/Results Single Integrator/Results set test/Configs/conf_single_integrator_0.py new file mode 100644 index 0000000..97d2fdd --- /dev/null +++ b/Results Single Integrator/Results set test/Configs/conf_single_integrator_0.py @@ -0,0 +1,202 @@ +import numpy as np + +system_id = 'single_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 100000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 25000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Single Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Single Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Single Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Single Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Single Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Single Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = 'set prova b' +NNs_path_rec = './Results Single Integrator/Results {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = 0 +update_step_counter_rec = 12000 + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + + + +### State parameters +nb_state = 2 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 2 +na = 2 +x_min = np.array([-np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0]), + np.array([10.0, 10.0, 0.0]), + np.array([-10.0, 10.0, 0.0]), + np.array([-10.0, -10.0, 0.0]), + np.array([12.0, 2.0, 0.0]), + np.array([12.0, -2.0, 0.0]), + np.array([15.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +tau_lower_bound = -6 # Action lower bound +tau_upper_bound = 6 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag + +# {'test_n': 0, 'seed': 0, 'system_id': 'single_integrator', 'recover_training_flag': False, 'nb_cpus': 15, 'w_S': 0.01} \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Configs/conf_single_integrator_1.py b/Results Single Integrator/Results set test/Configs/conf_single_integrator_1.py new file mode 100644 index 0000000..98b9188 --- /dev/null +++ b/Results Single Integrator/Results set test/Configs/conf_single_integrator_1.py @@ -0,0 +1,202 @@ +import numpy as np + +system_id = 'single_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 100000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 25000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Single Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Single Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Single Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Single Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Single Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Single Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = 'set prova b' +NNs_path_rec = './Results Single Integrator/Results {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = 0 +update_step_counter_rec = 12000 + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + + + +### State parameters +nb_state = 2 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 2 +na = 2 +x_min = np.array([-np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0]), + np.array([10.0, 10.0, 0.0]), + np.array([-10.0, 10.0, 0.0]), + np.array([-10.0, -10.0, 0.0]), + np.array([12.0, 2.0, 0.0]), + np.array([12.0, -2.0, 0.0]), + np.array([15.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +tau_lower_bound = -6 # Action lower bound +tau_upper_bound = 6 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag + +# {'test_n': 1, 'seed': 0, 'system_id': 'single_integrator', 'recover_training_flag': False, 'nb_cpus': 15, 'w_S': 0.01} \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Configs/conf_single_integrator_100.py b/Results Single Integrator/Results set test/Configs/conf_single_integrator_100.py new file mode 100644 index 0000000..fb53a8b --- /dev/null +++ b/Results Single Integrator/Results set test/Configs/conf_single_integrator_100.py @@ -0,0 +1,202 @@ +import numpy as np + +system_id = 'single_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 100000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 25000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Single Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Single Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Single Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Single Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Single Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Single Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = 'set prova b' +NNs_path_rec = './Results Single Integrator/Results {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = 0 +update_step_counter_rec = 12000 + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + + + +### State parameters +nb_state = 2 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 2 +na = 2 +x_min = np.array([-np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0]), + np.array([10.0, 10.0, 0.0]), + np.array([-10.0, 10.0, 0.0]), + np.array([-10.0, -10.0, 0.0]), + np.array([12.0, 2.0, 0.0]), + np.array([12.0, -2.0, 0.0]), + np.array([15.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +tau_lower_bound = -6 # Action lower bound +tau_upper_bound = 6 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag + +# {'test_n': 100, 'seed': 0, 'system_id': 'single_integrator', 'recover_training_flag': False, 'nb_cpus': 15, 'w_S': 0.01} \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Configs/conf_single_integrator_5.py b/Results Single Integrator/Results set test/Configs/conf_single_integrator_5.py new file mode 100644 index 0000000..c520365 --- /dev/null +++ b/Results Single Integrator/Results set test/Configs/conf_single_integrator_5.py @@ -0,0 +1,202 @@ +import numpy as np + +system_id = 'single_integrator' + +''' CACTO parameters ''' +EP_UPDATE = 200 # Number of episodes before updating critic and actor +NUPDATES = 100000 # Max NNs updates +UPDATE_LOOPS = np.arange(1000, 25000, 3000) # Number of updates of both critic and actor performed every EP_UPDATE episodes +NEPISODES = int(EP_UPDATE*len(UPDATE_LOOPS)) # Max training episodes +NLOOPS = len(UPDATE_LOOPS) # Number of algorithm loops +NSTEPS = 100 # Max episode length +CRITIC_LEARNING_RATE = 5e-4 # Learning rate for the critic network +ACTOR_LEARNING_RATE = 1e-3 # Learning rate for the policy network +REPLAY_SIZE = 2**16 # Size of the replay buffer +BATCH_SIZE = 128 # Size of the mini-batch + +# Set _steps_TD_N ONLY if MC not used +MC = 0 # Flag to use MC or TD(n) +if not MC: + UPDATE_RATE = 0.001 # Homotopy rate to update the target critic network if TD(n) is used + nsteps_TD_N = int(NSTEPS/4) # Number of lookahed steps if TD(n) is used + + +### Savings parameters +save_flag = 1 +if save_flag: + save_interval = 5000 # Save NNs interval +else: + save_interval = np.inf # Save NNs interval + +plot_flag = 1 +if plot_flag: + plot_rollout_interval = 400 # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = 6000 # plot.rollout() interval - diff_loc (# update) +else: + plot_rollout_interval = np.inf # plot.rollout() interval (# update) + plot_rollout_interval_diff_loc = np.inf # plot.rollout() interval - diff_loc (# update) + + + +### NNs parameters +critic_type = 'sine' # Activation function - critic (either relu, elu, sine, sine-elu) + +NH1 = 256 # 1st hidden layer size - actor +NH2 = 256 # 2nd hidden layer size - actor + +LR_SCHEDULE = 0 # Flag to use a scheduler for the learning rates +boundaries_schedule_LR_C = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of critic LR +values_schedule_LR_C = [CRITIC_LEARNING_RATE, + CRITIC_LEARNING_RATE/2, + CRITIC_LEARNING_RATE/4, + CRITIC_LEARNING_RATE/8, + CRITIC_LEARNING_RATE/16] +# Numbers of critic updates after which the actor LR is changed (based on values_schedule_LR_A) +boundaries_schedule_LR_A = [200*REPLAY_SIZE/BATCH_SIZE, + 300*REPLAY_SIZE/BATCH_SIZE, + 400*REPLAY_SIZE/BATCH_SIZE, + 500*REPLAY_SIZE/BATCH_SIZE] +# Values of actor LR +values_schedule_LR_A = [ACTOR_LEARNING_RATE, + ACTOR_LEARNING_RATE/2, + ACTOR_LEARNING_RATE/4, + ACTOR_LEARNING_RATE/8, + ACTOR_LEARNING_RATE/16] + +NORMALIZE_INPUTS = 1 # Flag to normalize inputs (state) + +kreg_l1_A = 1e-2 # Weight of L1 regularization in actor's network - kernel +kreg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - kernel +breg_l1_A = 1e-2 # Weight of L2 regularization in actor's network - bias +breg_l2_A = 1e-2 # Weight of L2 regularization in actor's network - bias +kreg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - kernel +kreg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - kernel +breg_l1_C = 1e-2 # Weight of L1 regularization in critic's network - bias +breg_l2_C = 1e-2 # Weight of L2 regularization in critic's network - bias + +### Buffer parameters +prioritized_replay_alpha = 0 # α determines how much prioritization is used, set to 0 to use a normal buffer. Used to define the probability of sampling transition i --> P(i) = p_i**α / sum(p_k**α) where p_i is the priority of transition i +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None # Therefore let's exploit the flexibility of annealing the amount of IS correction over time, by defining a schedule on the exponent β that from its initial value β0 reaches 1 only at the end of learning. +prioritized_replay_eps = 1e-2 # It's a small positive constant that prevents the edge-case of transitions not being revisited once their error is zero +fresh_factor = 0.95 # Refresh factor + + + +''' Cost function parameters ''' +### Obstacles parameters +XC1 = -2.0 # X coord center ellipse 1 +YC1 = 0.0 # Y coord center ellipse 1 +A1 = 6 # Width ellipse 1 +B1 = 10 # Height ellipse 1 +XC2 = 3.0 # X coord center ellipse 2 +YC2 = 4.0 # Y coord center ellipse 2 +A2 = 12 # Width ellipse 2 +B2 = 4 # Height ellipse 2 +XC3 = 3.0 # X coord center ellipse 2 +YC3 = -4.0 # Y coord center ellipse 2 +A3 = 12 # Width ellipse 2 +B3 = 4 # Height ellipse 2 +obs_param = np.array([XC1, YC1, XC2, YC2, XC3, YC3, A1, B1, A2, B2, A3, B3]) # Obstacle parameters vector + +### Weigths +w_d = 100 # Distance from target weight +w_u = 10 # Control effort weight +w_peak = 5e5 # Target threshold weight +w_ob = 5e6 # Obstacle weight +w_v = 0 # Velocity weight +weight = np.array([w_d, w_u, w_peak, w_ob, w_v]) # Weights vector (tmp) +cost_weights_running = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, w_u]) # Running cost weights vector +cost_weights_terminal = np.array([w_d, w_peak, 0., w_ob, w_ob, w_ob, 0]) # Terminal cost weights vector + +### SoftMax parameters +alpha = 50 # Soft abs coefficient (obstacle) +alpha2 = 5 # Soft abs coefficient (peak) +soft_max_param = np.array([alpha, alpha2]) # Soft parameters vector + +### Cost function parameters +offset_cost_fun = 0 # Reward/cost offset factor +scale_cost_fun = 1e-5 # Reward/cost scale factor (1e-5) +cost_funct_param = np.array([offset_cost_fun, scale_cost_fun]) + +### Target parameters +x_des = -7.0 # Target x position +y_des = 0.0 # Target y position +TARGET_STATE = np.array([x_des,y_des]) # Target position + + + +''' Path parameters ''' +test_set = 'set test' # Test id +Config_path = './Results Single Integrator/Results {}/Configs/'.format(test_set) # Configuration path +Fig_path = './Results Single Integrator/Results {}/Figures'.format(test_set) # Figure path +NNs_path = './Results Single Integrator/Results {}/NNs'.format(test_set) # NNs path +Log_path = './Results Single Integrator/Results {}/Log/'.format(test_set) # Log path +Code_path = './Results Single Integrator/Results {}/Code/'.format(test_set) # Code path +DictWS_path = './Results Single Integrator/Results {}/DictWS/'.format(test_set) # DictWS path +path_list = [Fig_path, NNs_path, Log_path, Code_path, DictWS_path] # Path list + +# Recover-training parameters +test_set_rec = 'set prova b' +NNs_path_rec = './Results Single Integrator/Results {}/NNs'.format(test_set_rec) # NNs path recover training +N_try_rec = 0 +update_step_counter_rec = 12000 + +''' System parameters ''' +env_RL = 0 # Flag RL environment: set True if RL_env and TO_env are different + +### Dynamics parameters +dt = 0.05 # Timestep + +simulate_coulomb_friction = 0 # To simulate friction +simulation_type = 'euler' # Either 'timestepping' or 'euler' +tau_coulomb_max = 0*np.ones(2) # Expressed as percentage of torque max +integration_scheme = 'E-Euler' # TO integration scheme - Either 'E-Euler' or 'SI-Euler' + + + +### State parameters +nb_state = 2 + 1 # State size (robot state size +1) +nq = None +nv = None +nx = 2 +na = 2 +x_min = np.array([-np.inf, -np.inf, 0]) # State lower bound vector +x_init_min = np.array([-15, -15, 0]) # State lower bound initial configuration array +x_max = np.array([np.inf, np.inf, np.inf]) # State upper bound vector +x_init_max = np.array([ 15, 15, (NSTEPS-1)*dt]) # State upper bound initial configuration array +state_norm_arr = np.array([15, 15, int(NSTEPS*dt)]) # Array used to normalize states + +# initial configurations for plot.rollout() +init_states_sim = [np.array([2.0, 0.0, 0.0]), + np.array([10.0, 0.0, 0.0]), + np.array([10.0, -10.0, 0.0]), + np.array([10.0, 10.0, 0.0]), + np.array([-10.0, 10.0, 0.0]), + np.array([-10.0, -10.0, 0.0]), + np.array([12.0, 2.0, 0.0]), + np.array([12.0, -2.0, 0.0]), + np.array([15.0, 0.0, 0.0])] + +### Action parameters +nb_action = 2 # Action size +tau_lower_bound = -6 # Action lower bound +tau_upper_bound = 6 # Action upper bound +u_min = tau_lower_bound*np.ones(nb_action) # Action lower bound vector +u_max = tau_upper_bound*np.ones(nb_action) # Action upper bound vector +w_b = 1/w_u + + + +### Plot parameters +fig_ax_lim = np.array([[-16, 16], [-16, 16]]) # Figure axis limit [x_min, x_max, y_min, y_max] + + + +profile = 0 # Profile flag + +# {'test_n': 5, 'seed': 10, 'system_id': 'single_integrator', 'recover_training_flag': False, 'nb_cpus': 1, 'w_S': 0.01} \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Configs/config0.txt b/Results Single Integrator/Results set test/Configs/config0.txt new file mode 100644 index 0000000..dfeb85b --- /dev/null +++ b/Results Single Integrator/Results set test/Configs/config0.txt @@ -0,0 +1,112 @@ +np = +system_id = single_integrator +EP_UPDATE = 200 +NUPDATES = 100000 +UPDATE_LOOPS = [ 1000 4000 7000 10000 13000 16000 19000 22000] +NEPISODES = 1600 +NLOOPS = 8 +NSTEPS = 100 +CRITIC_LEARNING_RATE = 0.0005 +ACTOR_LEARNING_RATE = 0.001 +REPLAY_SIZE = 65536 +BATCH_SIZE = 128 +MC = 0 +UPDATE_RATE = 0.001 +nsteps_TD_N = 25 +save_flag = 1 +save_interval = 5000 +plot_flag = 1 +plot_rollout_interval = 400 +plot_rollout_interval_diff_loc = 6000 +critic_type = sine +NH1 = 256 +NH2 = 256 +LR_SCHEDULE = 0 +boundaries_schedule_LR_C = [102400.0, 153600.0, 204800.0, 256000.0] +values_schedule_LR_C = [0.0005, 0.00025, 0.000125, 6.25e-05, 3.125e-05] +boundaries_schedule_LR_A = [102400.0, 153600.0, 204800.0, 256000.0] +values_schedule_LR_A = [0.001, 0.0005, 0.00025, 0.000125, 6.25e-05] +NORMALIZE_INPUTS = 1 +kreg_l1_A = 0.01 +kreg_l2_A = 0.01 +breg_l1_A = 0.01 +breg_l2_A = 0.01 +kreg_l1_C = 0.01 +kreg_l2_C = 0.01 +breg_l1_C = 0.01 +breg_l2_C = 0.01 +prioritized_replay_alpha = 0 +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None +prioritized_replay_eps = 0.01 +fresh_factor = 0.95 +XC1 = -2.0 +YC1 = 0.0 +A1 = 6 +B1 = 10 +XC2 = 3.0 +YC2 = 4.0 +A2 = 12 +B2 = 4 +XC3 = 3.0 +YC3 = -4.0 +A3 = 12 +B3 = 4 +obs_param = [-2. 0. 3. 4. 3. -4. 6. 10. 12. 4. 12. 4.] +w_d = 100 +w_u = 10 +w_peak = 500000.0 +w_ob = 5000000.0 +w_v = 0 +weight = [1.e+02 1.e+01 5.e+05 5.e+06 0.e+00] +cost_weights_running = [1.e+02 5.e+05 0.e+00 5.e+06 5.e+06 5.e+06 1.e+01] +cost_weights_terminal = [1.e+02 5.e+05 0.e+00 5.e+06 5.e+06 5.e+06 0.e+00] +alpha = 50 +alpha2 = 5 +soft_max_param = [50 5] +offset_cost_fun = 0 +scale_cost_fun = 1e-05 +cost_funct_param = [0.e+00 1.e-05] +x_des = -7.0 +y_des = 0.0 +TARGET_STATE = [-7. 0.] +test_set = set test +Config_path = ./Results Single Integrator/Results set test/Configs/ +Fig_path = ./Results Single Integrator/Results set test/Figures +NNs_path = ./Results Single Integrator/Results set test/NNs +Log_path = ./Results Single Integrator/Results set test/Log/ +Code_path = ./Results Single Integrator/Results set test/Code/ +DictWS_path = ./Results Single Integrator/Results set test/DictWS/ +path_list = ['./Results Single Integrator/Results set test/Figures', './Results Single Integrator/Results set test/NNs', './Results Single Integrator/Results set test/Log/', './Results Single Integrator/Results set test/Code/', './Results Single Integrator/Results set test/DictWS/'] +test_set_rec = set prova b +NNs_path_rec = ./Results Single Integrator/Results set prova b/NNs +N_try_rec = 0 +update_step_counter_rec = 12000 +env_RL = 0 +dt = 0.05 +simulate_coulomb_friction = 0 +simulation_type = euler +tau_coulomb_max = [0. 0.] +integration_scheme = E-Euler +nb_state = 3 +nq = None +nv = None +nx = 2 +na = 2 +x_min = [-inf -inf 0.] +x_init_min = [-15 -15 0] +x_max = [inf inf inf] +x_init_max = [15. 15. 4.95] +state_norm_arr = [15 15 5] +init_states_sim = [array([2., 0., 0.]), array([10., 0., 0.]), array([ 10., -10., 0.]), array([10., 10., 0.]), array([-10., 10., 0.]), array([-10., -10., 0.]), array([12., 2., 0.]), array([12., -2., 0.]), array([15., 0., 0.])] +nb_action = 2 +tau_lower_bound = -6 +tau_upper_bound = 6 +u_min = [-6. -6.] +u_max = [6. 6.] +w_b = 0.1 +fig_ax_lim = [[-16 16] + [-16 16]] +profile = 0 +Seed = 0 +w_S = 0.01 \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Configs/config1.txt b/Results Single Integrator/Results set test/Configs/config1.txt new file mode 100644 index 0000000..dfeb85b --- /dev/null +++ b/Results Single Integrator/Results set test/Configs/config1.txt @@ -0,0 +1,112 @@ +np = +system_id = single_integrator +EP_UPDATE = 200 +NUPDATES = 100000 +UPDATE_LOOPS = [ 1000 4000 7000 10000 13000 16000 19000 22000] +NEPISODES = 1600 +NLOOPS = 8 +NSTEPS = 100 +CRITIC_LEARNING_RATE = 0.0005 +ACTOR_LEARNING_RATE = 0.001 +REPLAY_SIZE = 65536 +BATCH_SIZE = 128 +MC = 0 +UPDATE_RATE = 0.001 +nsteps_TD_N = 25 +save_flag = 1 +save_interval = 5000 +plot_flag = 1 +plot_rollout_interval = 400 +plot_rollout_interval_diff_loc = 6000 +critic_type = sine +NH1 = 256 +NH2 = 256 +LR_SCHEDULE = 0 +boundaries_schedule_LR_C = [102400.0, 153600.0, 204800.0, 256000.0] +values_schedule_LR_C = [0.0005, 0.00025, 0.000125, 6.25e-05, 3.125e-05] +boundaries_schedule_LR_A = [102400.0, 153600.0, 204800.0, 256000.0] +values_schedule_LR_A = [0.001, 0.0005, 0.00025, 0.000125, 6.25e-05] +NORMALIZE_INPUTS = 1 +kreg_l1_A = 0.01 +kreg_l2_A = 0.01 +breg_l1_A = 0.01 +breg_l2_A = 0.01 +kreg_l1_C = 0.01 +kreg_l2_C = 0.01 +breg_l1_C = 0.01 +breg_l2_C = 0.01 +prioritized_replay_alpha = 0 +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None +prioritized_replay_eps = 0.01 +fresh_factor = 0.95 +XC1 = -2.0 +YC1 = 0.0 +A1 = 6 +B1 = 10 +XC2 = 3.0 +YC2 = 4.0 +A2 = 12 +B2 = 4 +XC3 = 3.0 +YC3 = -4.0 +A3 = 12 +B3 = 4 +obs_param = [-2. 0. 3. 4. 3. -4. 6. 10. 12. 4. 12. 4.] +w_d = 100 +w_u = 10 +w_peak = 500000.0 +w_ob = 5000000.0 +w_v = 0 +weight = [1.e+02 1.e+01 5.e+05 5.e+06 0.e+00] +cost_weights_running = [1.e+02 5.e+05 0.e+00 5.e+06 5.e+06 5.e+06 1.e+01] +cost_weights_terminal = [1.e+02 5.e+05 0.e+00 5.e+06 5.e+06 5.e+06 0.e+00] +alpha = 50 +alpha2 = 5 +soft_max_param = [50 5] +offset_cost_fun = 0 +scale_cost_fun = 1e-05 +cost_funct_param = [0.e+00 1.e-05] +x_des = -7.0 +y_des = 0.0 +TARGET_STATE = [-7. 0.] +test_set = set test +Config_path = ./Results Single Integrator/Results set test/Configs/ +Fig_path = ./Results Single Integrator/Results set test/Figures +NNs_path = ./Results Single Integrator/Results set test/NNs +Log_path = ./Results Single Integrator/Results set test/Log/ +Code_path = ./Results Single Integrator/Results set test/Code/ +DictWS_path = ./Results Single Integrator/Results set test/DictWS/ +path_list = ['./Results Single Integrator/Results set test/Figures', './Results Single Integrator/Results set test/NNs', './Results Single Integrator/Results set test/Log/', './Results Single Integrator/Results set test/Code/', './Results Single Integrator/Results set test/DictWS/'] +test_set_rec = set prova b +NNs_path_rec = ./Results Single Integrator/Results set prova b/NNs +N_try_rec = 0 +update_step_counter_rec = 12000 +env_RL = 0 +dt = 0.05 +simulate_coulomb_friction = 0 +simulation_type = euler +tau_coulomb_max = [0. 0.] +integration_scheme = E-Euler +nb_state = 3 +nq = None +nv = None +nx = 2 +na = 2 +x_min = [-inf -inf 0.] +x_init_min = [-15 -15 0] +x_max = [inf inf inf] +x_init_max = [15. 15. 4.95] +state_norm_arr = [15 15 5] +init_states_sim = [array([2., 0., 0.]), array([10., 0., 0.]), array([ 10., -10., 0.]), array([10., 10., 0.]), array([-10., 10., 0.]), array([-10., -10., 0.]), array([12., 2., 0.]), array([12., -2., 0.]), array([15., 0., 0.])] +nb_action = 2 +tau_lower_bound = -6 +tau_upper_bound = 6 +u_min = [-6. -6.] +u_max = [6. 6.] +w_b = 0.1 +fig_ax_lim = [[-16 16] + [-16 16]] +profile = 0 +Seed = 0 +w_S = 0.01 \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Configs/config100.txt b/Results Single Integrator/Results set test/Configs/config100.txt new file mode 100644 index 0000000..dfeb85b --- /dev/null +++ b/Results Single Integrator/Results set test/Configs/config100.txt @@ -0,0 +1,112 @@ +np = +system_id = single_integrator +EP_UPDATE = 200 +NUPDATES = 100000 +UPDATE_LOOPS = [ 1000 4000 7000 10000 13000 16000 19000 22000] +NEPISODES = 1600 +NLOOPS = 8 +NSTEPS = 100 +CRITIC_LEARNING_RATE = 0.0005 +ACTOR_LEARNING_RATE = 0.001 +REPLAY_SIZE = 65536 +BATCH_SIZE = 128 +MC = 0 +UPDATE_RATE = 0.001 +nsteps_TD_N = 25 +save_flag = 1 +save_interval = 5000 +plot_flag = 1 +plot_rollout_interval = 400 +plot_rollout_interval_diff_loc = 6000 +critic_type = sine +NH1 = 256 +NH2 = 256 +LR_SCHEDULE = 0 +boundaries_schedule_LR_C = [102400.0, 153600.0, 204800.0, 256000.0] +values_schedule_LR_C = [0.0005, 0.00025, 0.000125, 6.25e-05, 3.125e-05] +boundaries_schedule_LR_A = [102400.0, 153600.0, 204800.0, 256000.0] +values_schedule_LR_A = [0.001, 0.0005, 0.00025, 0.000125, 6.25e-05] +NORMALIZE_INPUTS = 1 +kreg_l1_A = 0.01 +kreg_l2_A = 0.01 +breg_l1_A = 0.01 +breg_l2_A = 0.01 +kreg_l1_C = 0.01 +kreg_l2_C = 0.01 +breg_l1_C = 0.01 +breg_l2_C = 0.01 +prioritized_replay_alpha = 0 +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None +prioritized_replay_eps = 0.01 +fresh_factor = 0.95 +XC1 = -2.0 +YC1 = 0.0 +A1 = 6 +B1 = 10 +XC2 = 3.0 +YC2 = 4.0 +A2 = 12 +B2 = 4 +XC3 = 3.0 +YC3 = -4.0 +A3 = 12 +B3 = 4 +obs_param = [-2. 0. 3. 4. 3. -4. 6. 10. 12. 4. 12. 4.] +w_d = 100 +w_u = 10 +w_peak = 500000.0 +w_ob = 5000000.0 +w_v = 0 +weight = [1.e+02 1.e+01 5.e+05 5.e+06 0.e+00] +cost_weights_running = [1.e+02 5.e+05 0.e+00 5.e+06 5.e+06 5.e+06 1.e+01] +cost_weights_terminal = [1.e+02 5.e+05 0.e+00 5.e+06 5.e+06 5.e+06 0.e+00] +alpha = 50 +alpha2 = 5 +soft_max_param = [50 5] +offset_cost_fun = 0 +scale_cost_fun = 1e-05 +cost_funct_param = [0.e+00 1.e-05] +x_des = -7.0 +y_des = 0.0 +TARGET_STATE = [-7. 0.] +test_set = set test +Config_path = ./Results Single Integrator/Results set test/Configs/ +Fig_path = ./Results Single Integrator/Results set test/Figures +NNs_path = ./Results Single Integrator/Results set test/NNs +Log_path = ./Results Single Integrator/Results set test/Log/ +Code_path = ./Results Single Integrator/Results set test/Code/ +DictWS_path = ./Results Single Integrator/Results set test/DictWS/ +path_list = ['./Results Single Integrator/Results set test/Figures', './Results Single Integrator/Results set test/NNs', './Results Single Integrator/Results set test/Log/', './Results Single Integrator/Results set test/Code/', './Results Single Integrator/Results set test/DictWS/'] +test_set_rec = set prova b +NNs_path_rec = ./Results Single Integrator/Results set prova b/NNs +N_try_rec = 0 +update_step_counter_rec = 12000 +env_RL = 0 +dt = 0.05 +simulate_coulomb_friction = 0 +simulation_type = euler +tau_coulomb_max = [0. 0.] +integration_scheme = E-Euler +nb_state = 3 +nq = None +nv = None +nx = 2 +na = 2 +x_min = [-inf -inf 0.] +x_init_min = [-15 -15 0] +x_max = [inf inf inf] +x_init_max = [15. 15. 4.95] +state_norm_arr = [15 15 5] +init_states_sim = [array([2., 0., 0.]), array([10., 0., 0.]), array([ 10., -10., 0.]), array([10., 10., 0.]), array([-10., 10., 0.]), array([-10., -10., 0.]), array([12., 2., 0.]), array([12., -2., 0.]), array([15., 0., 0.])] +nb_action = 2 +tau_lower_bound = -6 +tau_upper_bound = 6 +u_min = [-6. -6.] +u_max = [6. 6.] +w_b = 0.1 +fig_ax_lim = [[-16 16] + [-16 16]] +profile = 0 +Seed = 0 +w_S = 0.01 \ No newline at end of file diff --git a/Results Single Integrator/Results set test/Configs/config5.txt b/Results Single Integrator/Results set test/Configs/config5.txt new file mode 100644 index 0000000..e262d21 --- /dev/null +++ b/Results Single Integrator/Results set test/Configs/config5.txt @@ -0,0 +1,112 @@ +np = +system_id = single_integrator +EP_UPDATE = 200 +NUPDATES = 100000 +UPDATE_LOOPS = [ 1000 4000 7000 10000 13000 16000 19000 22000] +NEPISODES = 1600 +NLOOPS = 8 +NSTEPS = 100 +CRITIC_LEARNING_RATE = 0.0005 +ACTOR_LEARNING_RATE = 0.001 +REPLAY_SIZE = 65536 +BATCH_SIZE = 128 +MC = 0 +UPDATE_RATE = 0.001 +nsteps_TD_N = 25 +save_flag = 1 +save_interval = 5000 +plot_flag = 1 +plot_rollout_interval = 400 +plot_rollout_interval_diff_loc = 6000 +critic_type = sine +NH1 = 256 +NH2 = 256 +LR_SCHEDULE = 0 +boundaries_schedule_LR_C = [102400.0, 153600.0, 204800.0, 256000.0] +values_schedule_LR_C = [0.0005, 0.00025, 0.000125, 6.25e-05, 3.125e-05] +boundaries_schedule_LR_A = [102400.0, 153600.0, 204800.0, 256000.0] +values_schedule_LR_A = [0.001, 0.0005, 0.00025, 0.000125, 6.25e-05] +NORMALIZE_INPUTS = 1 +kreg_l1_A = 0.01 +kreg_l2_A = 0.01 +breg_l1_A = 0.01 +breg_l2_A = 0.01 +kreg_l1_C = 0.01 +kreg_l2_C = 0.01 +breg_l1_C = 0.01 +breg_l2_C = 0.01 +prioritized_replay_alpha = 0 +prioritized_replay_beta = 0.6 +prioritized_replay_beta_iters = None +prioritized_replay_eps = 0.01 +fresh_factor = 0.95 +XC1 = -2.0 +YC1 = 0.0 +A1 = 6 +B1 = 10 +XC2 = 3.0 +YC2 = 4.0 +A2 = 12 +B2 = 4 +XC3 = 3.0 +YC3 = -4.0 +A3 = 12 +B3 = 4 +obs_param = [-2. 0. 3. 4. 3. -4. 6. 10. 12. 4. 12. 4.] +w_d = 100 +w_u = 10 +w_peak = 500000.0 +w_ob = 5000000.0 +w_v = 0 +weight = [1.e+02 1.e+01 5.e+05 5.e+06 0.e+00] +cost_weights_running = [1.e+02 5.e+05 0.e+00 5.e+06 5.e+06 5.e+06 1.e+01] +cost_weights_terminal = [1.e+02 5.e+05 0.e+00 5.e+06 5.e+06 5.e+06 0.e+00] +alpha = 50 +alpha2 = 5 +soft_max_param = [50 5] +offset_cost_fun = 0 +scale_cost_fun = 1e-05 +cost_funct_param = [0.e+00 1.e-05] +x_des = -7.0 +y_des = 0.0 +TARGET_STATE = [-7. 0.] +test_set = set test +Config_path = ./Results Single Integrator/Results set test/Configs/ +Fig_path = ./Results Single Integrator/Results set test/Figures +NNs_path = ./Results Single Integrator/Results set test/NNs +Log_path = ./Results Single Integrator/Results set test/Log/ +Code_path = ./Results Single Integrator/Results set test/Code/ +DictWS_path = ./Results Single Integrator/Results set test/DictWS/ +path_list = ['./Results Single Integrator/Results set test/Figures', './Results Single Integrator/Results set test/NNs', './Results Single Integrator/Results set test/Log/', './Results Single Integrator/Results set test/Code/', './Results Single Integrator/Results set test/DictWS/'] +test_set_rec = set prova b +NNs_path_rec = ./Results Single Integrator/Results set prova b/NNs +N_try_rec = 0 +update_step_counter_rec = 12000 +env_RL = 0 +dt = 0.05 +simulate_coulomb_friction = 0 +simulation_type = euler +tau_coulomb_max = [0. 0.] +integration_scheme = E-Euler +nb_state = 3 +nq = None +nv = None +nx = 2 +na = 2 +x_min = [-inf -inf 0.] +x_init_min = [-15 -15 0] +x_max = [inf inf inf] +x_init_max = [15. 15. 4.95] 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sum. + """ + assert capacity > 0 and capacity & (capacity - 1) == 0, "capacity must be positive and a power of 2." + self._capacity = capacity + self._value = [neutral_element for _ in range(2 * capacity)] + self._operation = operation + + def _reduce_helper(self, start, end, node, node_start, node_end): + if start == node_start and end == node_end: + return self._value[node] + mid = (node_start + node_end) // 2 + if end <= mid: + return self._reduce_helper(start, end, 2 * node, node_start, mid) + else: + if mid + 1 <= start: + return self._reduce_helper(start, end, 2 * node + 1, mid + 1, node_end) + else: + return self._operation( + self._reduce_helper(start, mid, 2 * node, node_start, mid), + self._reduce_helper(mid + 1, end, 2 * node + 1, mid + 1, node_end) + ) + + def reduce(self, start=0, end=None): + """Returns result of applying `self.operation` + to a contiguous subsequence of the array. + + self.operation(arr[start], operation(arr[start+1], operation(... arr[end]))) + + Parameters + ---------- + start: int + beginning of the subsequence + end: int + end of the subsequences + + Returns + ------- + reduced: obj + result of reducing self.operation over the specified range of array elements. + """ + if end is None: + end = self._capacity + if end < 0: + end += self._capacity + end -= 1 + return self._reduce_helper(start, end, 1, 0, self._capacity - 1) + + def __setitem__(self, idx, val): + # index of the leaf + idx += self._capacity + self._value[idx] = val + idx //= 2 + while idx >= 1: + self._value[idx] = self._operation( + self._value[2 * idx], + self._value[2 * idx + 1] + ) + idx //= 2 + + def __getitem__(self, idx): + assert 0 <= idx < self._capacity + return self._value[self._capacity + idx] + + +class SumSegmentTree(SegmentTree): + def __init__(self, capacity): + super(SumSegmentTree, self).__init__( + capacity=capacity, + operation=operator.add, + neutral_element=0.0 + ) + + def sum(self, start=0, end=None): + """Returns arr[start] + ... + arr[end]""" + return super(SumSegmentTree, self).reduce(start, end) + + def find_prefixsum_idx(self, prefixsum): + """Find the highest index `i` in the array such that + sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum + + if array values are probabilities, this function + allows to sample indexes according to the discrete + probability efficiently. + + Parameters + ---------- + perfixsum: float + upperbound on the sum of array prefix + + Returns + ------- + idx: int + highest index satisfying the prefixsum constraint + """ + assert 0 <= prefixsum <= self.sum() + 1e-5 + idx = 1 + while idx < self._capacity: # while non-leaf + if self._value[2 * idx] > prefixsum: + idx = 2 * idx + else: + prefixsum -= self._value[2 * idx] + idx = 2 * idx + 1 + return idx - self._capacity + + +class MinSegmentTree(SegmentTree): + def __init__(self, capacity): + super(MinSegmentTree, self).__init__( + capacity=capacity, + operation=min, + neutral_element=float('inf') + ) + + def min(self, start=0, end=None): + """Returns min(arr[start], ..., arr[end])""" + + return super(MinSegmentTree, self).reduce(start, end)

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