From c3cea2df421d7230a0b3f911029bb451b4412fa5 Mon Sep 17 00:00:00 2001 From: "Takayoshi.Takayanagi" Date: Tue, 3 Sep 2024 15:27:41 +0900 Subject: [PATCH] Add IQL --- nnabla_rl/algorithms/__init__.py | 2 + nnabla_rl/algorithms/iql.py | 317 ++++++++++++++++++ nnabla_rl/environments/dummy.py | 13 + nnabla_rl/functions.py | 20 ++ nnabla_rl/model_trainers/policy/__init__.py | 1 + .../policy/awr_policy_trainer.py | 133 ++++++++ nnabla_rl/model_trainers/v_value/__init__.py | 4 + .../v_value/iql_v_function_trainer.py | 82 +++++ nnabla_rl/models/__init__.py | 3 + nnabla_rl/models/mujoco/policies.py | 77 +++++ nnabla_rl/models/mujoco/q_functions.py | 32 ++ nnabla_rl/models/mujoco/v_functions.py | 17 + .../algorithms/mujoco/iql/.gitignore | 2 + reproductions/algorithms/mujoco/iql/README.md | 91 +++++ .../iql/compute_expert_normalized_score.py | 131 ++++++++ .../algorithms/mujoco/iql/iql_reproduction.py | 191 +++++++++++ .../evaluation_result_average_scalar.tsv | 201 +++++++++++ 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reproductions/algorithms/mujoco/iql/reproduction_results/walker2d-medium-v2_results/seed-100/result.png diff --git a/nnabla_rl/algorithms/__init__.py b/nnabla_rl/algorithms/__init__.py index ebedd457..51c9b596 100644 --- a/nnabla_rl/algorithms/__init__.py +++ b/nnabla_rl/algorithms/__init__.py @@ -36,6 +36,7 @@ from nnabla_rl.algorithms.icml2018_sac import ICML2018SAC, ICML2018SACConfig from nnabla_rl.algorithms.icra2018_qtopt import ICRA2018QtOpt, ICRA2018QtOptConfig from nnabla_rl.algorithms.ilqr import iLQR, iLQRConfig +from nnabla_rl.algorithms.iql import IQL, IQLConfig from nnabla_rl.algorithms.iqn import IQN, IQNConfig from nnabla_rl.algorithms.lqr import LQR, LQRConfig from nnabla_rl.algorithms.mme_sac import MMESAC, MMESACConfig @@ -99,6 +100,7 @@ def get_class_of(name): register_algorithm(HyAR, HyARConfig) register_algorithm(ICML2018SAC, ICML2018SACConfig) register_algorithm(iLQR, iLQRConfig) +register_algorithm(IQL, IQLConfig) register_algorithm(IQN, IQNConfig) register_algorithm(LQR, LQRConfig) register_algorithm(MMESAC, MMESACConfig) diff --git a/nnabla_rl/algorithms/iql.py b/nnabla_rl/algorithms/iql.py new file mode 100644 index 00000000..97b7689f --- /dev/null +++ b/nnabla_rl/algorithms/iql.py @@ -0,0 +1,317 @@ +# Copyright 2024 Sony Group Corporation. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from typing import Dict, List, Union + +import gym + +import nnabla as nn +import nnabla.solvers as NS +import nnabla_rl.model_trainers as MT +from nnabla_rl.algorithm import Algorithm, AlgorithmConfig, eval_api +from nnabla_rl.algorithms.common_utils import _StochasticPolicyActionSelector +from nnabla_rl.builders import ModelBuilder, SolverBuilder +from nnabla_rl.environments.environment_info import EnvironmentInfo +from nnabla_rl.model_trainers.model_trainer import TrainingBatch +from nnabla_rl.models import IQLPolicy, IQLQFunction, IQLVFunction, QFunction, StochasticPolicy, VFunction +from nnabla_rl.utils import context +from nnabla_rl.utils.data import marshal_experiences +from nnabla_rl.utils.misc import sync_model + + +@dataclass +class IQLConfig(AlgorithmConfig): + """List of configurations for IQL algorithm. + + Args: + gamma (float): discount factor of reward. Defaults to 0.99. + learning_rate (float): learning rate which is set to all solvers. \ + You can customize/override the learning rate for each solver by implementing the \ + (:py:class:`SolverBuilder `) by yourself. \ + Defaults to 0.0003. + batch_size (int): training batch size. Defaults to 256. + tau (float): target network's parameter update coefficient. Defaults to 0.005. + expectile (float): the expectile value for expectile regression loss. Defaults to 0.7. + beta (float): the temperature parameter of advantage weight. Defaults to 3.0 + advantage_clip (Optional[float]): the value for clipping advantage weight. Defaults to 100.0 + """ + + gamma: float = 0.99 + learning_rate: float = 3.0 * 1e-4 + batch_size: int = 256 + tau: float = 0.005 + expectile: float = 0.7 + beta: float = 3.0 + advantage_clip: float = 100.0 + + def __post_init__(self): + """__post_init__ + + Check set values are in valid range. + """ + self._assert_between(self.gamma, 0.0, 1.0, "gamma") + self._assert_between(self.tau, 0.0, 1.0, "tau") + self._assert_positive(self.batch_size, "batch_size") + self._assert_between(self.expectile, 0.0, 1.0, "expectile") + + +class DefaultQFunctionBuilder(ModelBuilder[QFunction]): + def build_model( # type: ignore[override] + self, + scope_name: str, + env_info: EnvironmentInfo, + algorithm_config: IQLConfig, + **kwargs, + ) -> QFunction: + return IQLQFunction(scope_name) + + +class DefaultVFunctionBuilder(ModelBuilder[VFunction]): + def build_model( # type: ignore[override] + self, + scope_name: str, + env_info: EnvironmentInfo, + algorithm_config: IQLConfig, + **kwargs, + ) -> VFunction: + return IQLVFunction(scope_name) + + +class DefaultPolicyBuilder(ModelBuilder[StochasticPolicy]): + def build_model( # type: ignore[override] + self, + scope_name: str, + env_info: EnvironmentInfo, + algorithm_config: IQLConfig, + **kwargs, + ) -> StochasticPolicy: + return IQLPolicy(scope_name, action_dim=env_info.action_dim) + + +class DefaultSolverBuilder(SolverBuilder): + def build_solver(self, env_info: EnvironmentInfo, algorithm_config: IQLConfig, **kwargs): # type: ignore[override] + return NS.Adam(alpha=algorithm_config.learning_rate) + + +class IQL(Algorithm): + """Implicit Q-learning (IQL) algorithm. + + This class implements the Implicit Q-learning (IQL) algorithm + proposed by I. Kostrikov, et al. in the paper: "OFFLINE REINFORCEMENT LEARNING WITH IMPLICIT Q-LEARNING" + For details see: https://arxiv.org/abs/2110.06169 + + This algorithm only supports offline training. + + Args: + env_or_env_info \ + (gym.Env or :py:class:`EnvironmentInfo `): + the environment to train or environment info + config (:py:class:`IQLConfig `): + configuration of the IQL algorithm + q_function_builder (:py:class:`ModelBuilder[QFunction] `): + builder of q-function models + q_solver_builder (:py:class:`SolverBuilder `): + builder for q-function solvers + v_function_builder (:py:class:`ModelBuilder[VFunction] `): + builder of v-function models + v_solver_builder (:py:class:`SolverBuilder `): + builder for v-function solvers + policy_builder (:py:class:`ModelBuilder[StochasticPolicy] `): + builder of policy models + policy_solver_builder (:py:class:`SolverBuilder `): + builder for policy solvers + """ + + # type declarations to type check with mypy + # NOTE: declared variables are instance variable and NOT class variable, unless it is marked with ClassVar + # See https://mypy.readthedocs.io/en/stable/class_basics.html for details + _config: IQLConfig + _train_q_functions: List[QFunction] + _train_q_solvers: Dict[str, nn.solver.Solver] + _target_q_functions: List[QFunction] + _v_function: VFunction + _v_solver: nn.solver.Solver + _pi: StochasticPolicy + _pi_solver: nn.solver.Solver + + def __init__( + self, + env_or_env_info: Union[gym.Env, EnvironmentInfo], + config: IQLConfig = IQLConfig(), + q_function_builder: ModelBuilder[QFunction] = DefaultQFunctionBuilder(), + q_solver_builder: SolverBuilder = DefaultSolverBuilder(), + v_function_builder: ModelBuilder[VFunction] = DefaultVFunctionBuilder(), + v_solver_builder: SolverBuilder = DefaultSolverBuilder(), + policy_builder: ModelBuilder[StochasticPolicy] = DefaultPolicyBuilder(), + policy_solver_builder: SolverBuilder = DefaultSolverBuilder(), + ): + super(IQL, self).__init__(env_or_env_info, config=config) + with nn.context_scope(context.get_nnabla_context(self._config.gpu_id)): + self._train_q_functions = self._build_q_functions(q_function_builder) + self._train_q_solvers = { + q.scope_name: q_solver_builder(self._env_info, self._config) for q in self._train_q_functions + } + self._target_q_functions = [q.deepcopy("target_" + q.scope_name) for q in self._train_q_functions] + + self._v_function = v_function_builder( + scope_name="v", env_info=self._env_info, algorithm_config=self._config + ) + self._v_solver = v_solver_builder(self._env_info, self._config) + + self._pi = policy_builder(scope_name="pi", env_info=self._env_info, algorithm_config=self._config) + self._pi_solver = policy_solver_builder(self._env_info, self._config) + + self._evaluation_actor = self._setup_evaluation_actor() + + @eval_api + def compute_eval_action(self, state, *, begin_of_episode=False, extra_info={}): + with nn.context_scope(context.get_nnabla_context(self._config.gpu_id)): + action, _ = self._evaluation_action_selector(state, begin_of_episode=begin_of_episode) + return action + + def _setup_evaluation_actor(self): + return _StochasticPolicyActionSelector(self._env_info, self._pi.shallowcopy(), deterministic=True) + + def _evaluation_action_selector(self, s, *, begin_of_episode=False): + return self._evaluation_actor(s, begin_of_episode=begin_of_episode) + + def _build_q_functions(self, q_function_builder): + q_functions = [] + for i in range(2): + q = q_function_builder(scope_name=f"q{i+1}", env_info=self._env_info, algorithm_config=self._config) + q_functions.append(q) + return q_functions + + def _before_training_start(self, env_or_buffer): + # set context globally to ensure that the training runs on configured gpu + context.set_nnabla_context(self._config.gpu_id) + self._v_function_trainer = self._setup_v_function_training(env_or_buffer) + self._q_function_trainer = self._setup_q_function_training(env_or_buffer) + self._policy_trainer = self._setup_policy_training(env_or_buffer) + + def _setup_policy_training(self, env_or_buffer): + policy_trainer_config = MT.policy_trainers.AWRPolicyTrainerConfig( + beta=self._config.beta, advantage_clip=self._config.advantage_clip + ) + policy_trainer = MT.policy_trainers.AWRPolicyTrainer( + models=self._pi, + solvers={self._pi.scope_name: self._pi_solver}, + q_functions=self._target_q_functions, + v_function=self._v_function, + env_info=self._env_info, + config=policy_trainer_config, + ) + return policy_trainer + + def _setup_q_function_training(self, env_or_buffer): + q_function_trainer_config = MT.q_value_trainers.VTargetedQTrainerConfig( + reduction_method="mean", q_loss_scalar=1.0 + ) + q_function_trainer = MT.q_value_trainers.VTargetedQTrainer( + train_functions=self._train_q_functions, + solvers=self._train_q_solvers, + target_functions=self._v_function, + env_info=self._env_info, + config=q_function_trainer_config, + ) + for q, target_q in zip(self._train_q_functions, self._target_q_functions): + sync_model(q, target_q, 1.0) + return q_function_trainer + + def _setup_v_function_training(self, env_or_buffer): + v_function_trainer_config = MT.v_value_trainers.IQLVFunctionTrainerConfig(expectile=self._config.expectile) + v_function_trainer = MT.v_value_trainers.IQLVFunctionTrainer( + models=self._v_function, + solvers={self._v_function.scope_name: self._v_solver}, + target_functions=self._target_q_functions, + env_info=self._env_info, + config=v_function_trainer_config, + ) + return v_function_trainer + + def _run_online_training_iteration(self, env): + # Not support online training + raise NotImplementedError + + def _run_offline_training_iteration(self, buffer): + self._iql_training(buffer) + + def _iql_training(self, replay_buffer): + experiences, info = replay_buffer.sample(self._config.batch_size) + (s, a, r, non_terminal, s_next, *_) = marshal_experiences(experiences) + batch = TrainingBatch( + batch_size=self._config.batch_size, + s_current=s, + a_current=a, + gamma=self._config.gamma, + reward=r, + non_terminal=non_terminal, + s_next=s_next, + weight=info["weights"], + ) + + self._v_function_trainer_state = self._v_function_trainer.train(batch) + + self._policy_trainer_state = self._policy_trainer.train(batch) + + self._q_function_trainer_state = self._q_function_trainer.train(batch) + for q, target_q in zip(self._train_q_functions, self._target_q_functions): + sync_model(q, target_q, tau=self._config.tau) + td_errors = self._q_function_trainer_state["td_errors"] + replay_buffer.update_priorities(td_errors) + + def _models(self): + models = [self._v_function, *self._train_q_functions, self._pi] + return {model.scope_name: model for model in models} + + def _solvers(self): + solvers = {} + solvers[self._pi.scope_name] = self._pi_solver + solvers[self._v_function.scope_name] = self._v_solver + solvers.update(self._train_q_solvers) + return solvers + + @classmethod + def is_rnn_supported(self): + return False + + @classmethod + def is_supported_env(cls, env_or_env_info): + env_info = ( + EnvironmentInfo.from_env(env_or_env_info) if isinstance(env_or_env_info, gym.Env) else env_or_env_info + ) + return not env_info.is_discrete_action_env() and not env_info.is_tuple_action_env() + + @property + def latest_iteration_state(self): + latest_iteration_state = super(IQL, self).latest_iteration_state + if hasattr(self, "_v_function_trainer_state"): + latest_iteration_state["scalar"].update({"v_loss": float(self._v_function_trainer_state["v_loss"])}) + if hasattr(self, "_q_function_trainer_state"): + latest_iteration_state["scalar"].update({"q_loss": float(self._q_function_trainer_state["q_loss"])}) + latest_iteration_state["histogram"].update( + {"td_errors": self._q_function_trainer_state["td_errors"].flatten()} + ) + if hasattr(self, "_policy_trainer_state"): + latest_iteration_state["scalar"].update({"pi_loss": float(self._policy_trainer_state["pi_loss"])}) + return latest_iteration_state + + @property + def trainers(self): + return { + "v_function": self._v_function_trainer, + "q_function": self._q_function_trainer, + "policy": self._policy_trainer, + } diff --git a/nnabla_rl/environments/dummy.py b/nnabla_rl/environments/dummy.py index f6247b80..4d0f361d 100644 --- a/nnabla_rl/environments/dummy.py +++ b/nnabla_rl/environments/dummy.py @@ -221,6 +221,19 @@ def get_dataset(self): dataset["timeouts"] = np.zeros((datasize, 1)) return dataset + def get_qlearning_dataset(self): + dataset = self.get_dataset() + + N = dataset["rewards"].shape[0] + + return { + "observations": dataset["observations"][: N - 1].astype(np.float32), + "actions": dataset["actions"][: N - 1].astype(np.float32), + "next_observations": dataset["observations"][1:].astype(np.float32), + "rewards": dataset["rewards"][: N - 1].astype(np.float32), + "terminals": dataset["terminals"][: N - 1].astype(bool), + } + class DummyDMControlEnv(DummyMujocoEnv): pass diff --git a/nnabla_rl/functions.py b/nnabla_rl/functions.py index 08c8054e..f0248b31 100644 --- a/nnabla_rl/functions.py +++ b/nnabla_rl/functions.py @@ -691,3 +691,23 @@ def compute_std(var: nn.Variable, epsilon: float, mode_for_floating_point_error: else: raise ValueError return std + + +def expectile_regression(x: nn.Variable, tau: float) -> nn.Variable: + """Expectile regression function. + + math:: + L_2^\\tau(u) = |\\tau - 1(x < 0)|x^2 + + Args: + x (nn.Variable): the input variable. + tau (float): the expectile level. It should be between 0 and 1. + + Returns: + nn.Variable: the expectile regression loss. + """ + ones_var = nn.Variable.from_numpy_array(np.ones(x.shape)) + weight = NF.where( + NF.greater_scalar(x.get_unlinked_variable(need_grad=False), 0.0), ones_var * tau, ones_var * (1 - tau) + ) + return weight * NF.pow_scalar(x, 2) diff --git a/nnabla_rl/model_trainers/policy/__init__.py b/nnabla_rl/model_trainers/policy/__init__.py index 09a79070..510b8b7d 100644 --- a/nnabla_rl/model_trainers/policy/__init__.py +++ b/nnabla_rl/model_trainers/policy/__init__.py @@ -14,6 +14,7 @@ from nnabla_rl.model_trainers.policy.a2c_policy_trainer import A2CPolicyTrainer, A2CPolicyTrainerConfig # noqa from nnabla_rl.model_trainers.policy.amp_policy_trainer import AMPPolicyTrainer, AMPPolicyTrainerConfig # noqa +from nnabla_rl.model_trainers.policy.awr_policy_trainer import AWRPolicyTrainer, AWRPolicyTrainerConfig # noqa from nnabla_rl.model_trainers.policy.bear_policy_trainer import BEARPolicyTrainer, BEARPolicyTrainerConfig # noqa from nnabla_rl.model_trainers.policy.demme_policy_trainer import DEMMEPolicyTrainer, DEMMEPolicyTrainerConfig # noqa from nnabla_rl.model_trainers.policy.dpg_policy_trainer import DPGPolicyTrainer, DPGPolicyTrainerConfig # noqa diff --git a/nnabla_rl/model_trainers/policy/awr_policy_trainer.py b/nnabla_rl/model_trainers/policy/awr_policy_trainer.py new file mode 100644 index 00000000..c1dddf6d --- /dev/null +++ b/nnabla_rl/model_trainers/policy/awr_policy_trainer.py @@ -0,0 +1,133 @@ +# Copyright 2024 Sony Group Corporation. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from typing import Dict, Optional, Sequence, Union + +import numpy as np + +import nnabla as nn +import nnabla.functions as NF +import nnabla_rl.functions as RNF +from nnabla_rl.environments.environment_info import EnvironmentInfo +from nnabla_rl.model_trainers.model_trainer import ( + LossIntegration, + ModelTrainer, + TrainerConfig, + TrainingBatch, + TrainingVariables, +) +from nnabla_rl.models import Model, QFunction, StochasticPolicy, VFunction +from nnabla_rl.utils.data import convert_to_list_if_not_list, set_data_to_variable +from nnabla_rl.utils.misc import create_variable + + +@dataclass +class AWRPolicyTrainerConfig(TrainerConfig): + """List of configuration for AWRPolicyTrainer. + + Args: + beta (float): the temperature parameter of advantage weight. Defaults to 1.0 + advantage_clip (Optional[float]): the value for clipping advantage weight. Defaults to 100.0 + """ + + beta: float = 1.0 + advantage_clip: Optional[float] = 100.0 + + +class AWRPolicyTrainer(ModelTrainer): + """Policy Trainer with Advantage-Weighted Regression (AWR)""" + + _pi_loss: nn.Variable + _config: AWRPolicyTrainerConfig + + def __init__( + self, + models: Union[StochasticPolicy, Sequence[StochasticPolicy]], + solvers: Dict[str, nn.solver.Solver], + q_functions: Union[QFunction, Sequence[QFunction]], + v_function: VFunction, + env_info: EnvironmentInfo, + config: AWRPolicyTrainerConfig = AWRPolicyTrainerConfig(), + ): + self._q_functions = convert_to_list_if_not_list(q_functions) + self._v_function = v_function + super(AWRPolicyTrainer, self).__init__(models, solvers, env_info, config) + + def _build_training_graph(self, models: Sequence[Model], training_variables: TrainingVariables): + self._pi_loss = 0 + ignore_intermediate_loss = self._config.loss_integration is LossIntegration.LAST_TIMESTEP_ONLY + for step_index, variables in enumerate(training_variables): + is_burn_in_steps = step_index < self._config.burn_in_steps + is_intermediate_steps = step_index < self._config.burn_in_steps + self._config.unroll_steps - 1 + ignore_loss = is_burn_in_steps or (is_intermediate_steps and ignore_intermediate_loss) + self._pi_loss += self._build_one_step_graph(models, variables, ignore_loss=ignore_loss) + + def _build_one_step_graph(self, models: Sequence[Model], training_variables: TrainingVariables, ignore_loss: bool): + advantage_weight = self._compute_advantage_weight(training_variables) + advantage_weight.need_grad = False + one_step_pi_loss = 0.0 + for policy in models: + assert isinstance(policy, StochasticPolicy) + + # Actor optimization graph + policy_distribution = policy.pi(training_variables.s_current) + log_pi = policy_distribution.log_prob(training_variables.a_current) + one_step_pi_loss += 0.0 if ignore_loss else NF.mean(advantage_weight * log_pi) * (-1) + return one_step_pi_loss + + def _compute_advantage_weight(self, training_variables: TrainingVariables): + q_values = [] + for q_function in self._q_functions: + q_values.append(q_function.q(training_variables.s_current, training_variables.a_current)) + min_q = RNF.minimum_n(q_values) + v = self._v_function.v(training_variables.s_current) + advantage = min_q - v + advantage_weight = NF.exp(self._config.beta * advantage) + if self._config.advantage_clip is not None: + advantage_weight = NF.minimum_scalar(advantage_weight, val=self._config.advantage_clip) + return advantage_weight + + def _update_model( + self, + models: Sequence[Model], + solvers: Dict[str, nn.solver.Solver], + batch: TrainingBatch, + training_variables: TrainingVariables, + **kwargs, + ) -> Dict[str, np.ndarray]: + for t, b in zip(training_variables, batch): + set_data_to_variable(t.s_current, b.s_current) + set_data_to_variable(t.a_current, b.a_current) + + # update model + for solver in solvers.values(): + solver.zero_grad() + self._pi_loss.forward() + self._pi_loss.backward() + for solver in solvers.values(): + solver.update() + trainer_state = {} + trainer_state["pi_loss"] = self._pi_loss.d.copy() + return trainer_state + + def _setup_training_variables(self, batch_size): + # Training input variables + s_current_var = create_variable(batch_size, self._env_info.state_shape) + a_current_var = create_variable(batch_size, self._env_info.action_shape) + return TrainingVariables(batch_size, s_current_var, a_current_var) + + @property + def loss_variables(self) -> Dict[str, nn.Variable]: + return {"pi_loss": self._pi_loss} diff --git a/nnabla_rl/model_trainers/v_value/__init__.py b/nnabla_rl/model_trainers/v_value/__init__.py index f3defa84..a55c259a 100644 --- a/nnabla_rl/model_trainers/v_value/__init__.py +++ b/nnabla_rl/model_trainers/v_value/__init__.py @@ -17,3 +17,7 @@ from nnabla_rl.model_trainers.v_value.mme_v_trainer import MMEVTrainer, MMEVTrainerConfig # noqa from nnabla_rl.model_trainers.v_value.monte_carlo_v_trainer import MonteCarloVTrainer, MonteCarloVTrainerConfig # noqa from nnabla_rl.model_trainers.v_value.soft_v_trainer import SoftVTrainer, SoftVTrainerConfig # noqa +from nnabla_rl.model_trainers.v_value.iql_v_function_trainer import ( # noqa + IQLVFunctionTrainer, + IQLVFunctionTrainerConfig, +) diff --git a/nnabla_rl/model_trainers/v_value/iql_v_function_trainer.py b/nnabla_rl/model_trainers/v_value/iql_v_function_trainer.py new file mode 100644 index 00000000..f2b514cf --- /dev/null +++ b/nnabla_rl/model_trainers/v_value/iql_v_function_trainer.py @@ -0,0 +1,82 @@ +# Copyright 2024 Sony Group Corporation. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from typing import Dict, Sequence, Union + +import numpy as np + +import nnabla as nn +import nnabla_rl.functions as RNF +from nnabla_rl.environments.environment_info import EnvironmentInfo +from nnabla_rl.model_trainers.model_trainer import TrainingBatch, TrainingVariables +from nnabla_rl.model_trainers.v_value.v_function_trainer import VFunctionTrainer, VFunctionTrainerConfig +from nnabla_rl.models import Model, QFunction, VFunction +from nnabla_rl.utils.data import convert_to_list_if_not_list, set_data_to_variable +from nnabla_rl.utils.misc import create_variable + + +@dataclass +class IQLVFunctionTrainerConfig(VFunctionTrainerConfig): + expectile: float = 0.7 + + +class IQLVFunctionTrainer(VFunctionTrainer): + _config: IQLVFunctionTrainerConfig + + def __init__( + self, + models: Union[VFunction, Sequence[VFunction]], + solvers: Dict[str, nn.solver.Solver], + target_functions: Union[QFunction, Sequence[QFunction]], + env_info: EnvironmentInfo, + config: IQLVFunctionTrainerConfig = IQLVFunctionTrainerConfig(), + ): + self._target_functions = convert_to_list_if_not_list(target_functions) + super().__init__(models, solvers, env_info, config) + + def _update_model( + self, + models: Sequence[Model], + solvers: Dict[str, nn.solver.Solver], + batch: TrainingBatch, + training_variables: TrainingVariables, + **kwargs, + ) -> Dict[str, np.ndarray]: + for t, b in zip(training_variables, batch): + set_data_to_variable(t.a_current, b.a_current) + + return super()._update_model(models, solvers, batch, training_variables, **kwargs) + + def _compute_target(self, training_variables: TrainingVariables, **kwargs): + s_current = training_variables.s_current + a_current = training_variables.a_current + q_values = [] + for q_function in self._target_functions: + q_values.append(q_function.q(s_current, a_current)) + target_q = RNF.minimum_n(q_values) + return target_q + + def _compute_loss( + self, model: VFunction, target_value: nn.Variable, training_variables: TrainingVariables + ) -> nn.Variable: + v_value = model.v(training_variables.s_current) + td_error = target_value - v_value + return RNF.expectile_regression(td_error, self._config.expectile) + + def _setup_training_variables(self, batch_size: int) -> TrainingVariables: + training_variables = super()._setup_training_variables(batch_size) + training_variables.a_current = create_variable(batch_size, self._env_info.action_shape) + + return training_variables diff --git a/nnabla_rl/models/__init__.py b/nnabla_rl/models/__init__.py index a0978129..e693e10b 100644 --- a/nnabla_rl/models/__init__.py +++ b/nnabla_rl/models/__init__.py @@ -54,6 +54,7 @@ SACDQFunction, HERQFunction, XQLQFunction, + IQLQFunction, ) from nnabla_rl.models.mujoco.decision_transformers import MujocoDecisionTransformer # noqa from nnabla_rl.models.mujoco.distributional_functions import QRSACQuantileDistributionFunction # noqa @@ -61,6 +62,7 @@ from nnabla_rl.models.mujoco.v_functions import PPOVFunction as PPOMujocoVFunction # noqa from nnabla_rl.models.mujoco.v_functions import GAILVFunction # noqa from nnabla_rl.models.mujoco.v_functions import XQLVFunction # noqa +from nnabla_rl.models.mujoco.v_functions import IQLVFunction # noqa from nnabla_rl.models.mujoco.encoders import UnsquashedVariationalAutoEncoder, BCQVariationalAutoEncoder # noqa from nnabla_rl.models.mujoco.perturbators import BCQPerturbator # noqa from nnabla_rl.models.mujoco.policies import ICML2015TRPOPolicy as ICML2015TRPOMujocoPolicy # noqa @@ -69,6 +71,7 @@ from nnabla_rl.models.mujoco.policies import HERPolicy # noqa from nnabla_rl.models.mujoco.policies import ATRPOPolicy # noqa from nnabla_rl.models.mujoco.policies import XQLPolicy # noqa +from nnabla_rl.models.mujoco.policies import IQLPolicy # noqa from nnabla_rl.models.mujoco.reward_functions import GAILDiscriminator # noqa from nnabla_rl.models.atari.decision_transformers import AtariDecisionTransformer # noqa from nnabla_rl.models.atari.policies import PPOPolicy as PPOAtariPolicy # noqa diff --git a/nnabla_rl/models/mujoco/policies.py b/nnabla_rl/models/mujoco/policies.py index edda6f4c..ee4e5adf 100644 --- a/nnabla_rl/models/mujoco/policies.py +++ b/nnabla_rl/models/mujoco/policies.py @@ -385,3 +385,80 @@ def pi(self, s: nn.Variable) -> Distribution: ln_sigma = NF.clip_by_value(ln_sigma, min=self._min_log_sigma, max=self._max_log_sigma) ln_var = ln_sigma * 2.0 return D.Gaussian(mean=mean, ln_var=ln_var) + + +class IQLPolicy(StochasticPolicy): + """Actor model proposed by Ilya Kostrikov in IQL paper for mujoco + environment. + + See: https://arxiv.org/pdf/2110.06169 + """ + + # type declarations to type check with mypy + # NOTE: declared variables are instance variable and NOT class variable, unless it is marked with ClassVar + # See https://mypy.readthedocs.io/en/stable/class_basics.html for details + _action_dim: int + _clip_log_sigma: bool + _min_log_sigma: float + _max_log_sigma: float + + def __init__( + self, + scope_name: str, + action_dim: int, + clip_log_sigma: bool = True, + min_log_sigma: float = -5.0, + max_log_sigma: float = 2.0, + log_sigma_scale: float = 1.0e-3, + state_dependent_sigma: bool = False, + tanh_squash_distribution: bool = False, + temperature: float = 1.0, + ): + super(IQLPolicy, self).__init__(scope_name) + self._action_dim = action_dim + self._clip_log_sigma = clip_log_sigma + self._min_log_sigma = min_log_sigma + self._max_log_sigma = max_log_sigma + self._log_sigma_scale = log_sigma_scale + self._state_dependent_sigma = state_dependent_sigma + self._tanh_squash_distribution = tanh_squash_distribution + self._log_temperature = np.log(temperature) + + def pi(self, s: nn.Variable) -> Distribution: + with nn.parameter_scope(self.scope_name): + h = NPF.affine(s, w_init=NI.OrthogonalInitializer(gain=np.sqrt(2.0)), n_outmaps=256, name="linear1") + h = NF.relu(x=h) + h = NPF.affine(h, w_init=NI.OrthogonalInitializer(gain=np.sqrt(2.0)), n_outmaps=256, name="linear2") + h = NF.relu(x=h) + + mean = NPF.affine( + h, + w_init=NI.OrthogonalInitializer(gain=np.sqrt(2.0)), + n_outmaps=self._action_dim, + name="linear_mean", + ) + + if self._state_dependent_sigma: + ln_sigma = NPF.affine( + h, + w_init=NI.OrthogonalInitializer(gain=self._log_sigma_scale), + n_outmaps=self._action_dim, + name="linear_sigma", + ) + else: + ln_sigma = get_parameter_or_create( + "ln_sigma", shape=(1, self._action_dim), initializer=NI.ConstantInitializer(0.0) + ) + ln_sigma = NF.broadcast(ln_sigma, (s.shape[0], self._action_dim)) + if self._clip_log_sigma: + ln_sigma = NF.clip_by_value(ln_sigma, min=self._min_log_sigma, max=self._max_log_sigma) + ln_sigma += self._log_temperature + assert mean.shape == ln_sigma.shape + assert mean.shape == (s.shape[0], self._action_dim) + ln_var = ln_sigma * 2.0 + + if self._tanh_squash_distribution: + return D.SquashedGaussian(mean=mean, ln_var=ln_var) + else: + mean = NF.tanh(mean) + return D.Gaussian(mean=mean, ln_var=ln_var) diff --git a/nnabla_rl/models/mujoco/q_functions.py b/nnabla_rl/models/mujoco/q_functions.py index bed1b8b5..5f738aaa 100644 --- a/nnabla_rl/models/mujoco/q_functions.py +++ b/nnabla_rl/models/mujoco/q_functions.py @@ -194,3 +194,35 @@ def max_q(self, s: nn.Variable) -> nn.Variable: assert self._optimal_policy, "Optimal policy is not set!" optimal_action = self._optimal_policy.pi(s) return self.q(s, optimal_action) + + +class IQLQFunction(ContinuousQFunction): + """QFunciton model proposed by Ilya Kostrikov in IQL paper for mujoco + environment. + + See: https://arxiv.org/pdf/2110.06169 + """ + + # type declarations to type check with mypy + # NOTE: declared variables are instance variable and NOT class variable, unless it is marked with ClassVar + # See https://mypy.readthedocs.io/en/stable/class_basics.html for details + _optimal_policy: Optional[DeterministicPolicy] + + def __init__(self, scope_name, optimal_policy: Optional[DeterministicPolicy] = None): + super(IQLQFunction, self).__init__(scope_name) + self._optimal_policy = optimal_policy + + def q(self, s: nn.Variable, a: nn.Variable) -> nn.Variable: + with nn.parameter_scope(self.scope_name): + h = NF.concatenate(s, a) + h = NPF.affine(h, w_init=NI.OrthogonalInitializer(gain=np.sqrt(2.0)), n_outmaps=256, name="linear1") + h = NF.relu(x=h) + h = NPF.affine(h, w_init=NI.OrthogonalInitializer(gain=np.sqrt(2.0)), n_outmaps=256, name="linear2") + h = NF.relu(x=h) + h = NPF.affine(h, w_init=NI.OrthogonalInitializer(gain=np.sqrt(2.0)), n_outmaps=1, name="linear3") + return h + + def max_q(self, s: nn.Variable) -> nn.Variable: + assert self._optimal_policy, "Optimal policy is not set!" + optimal_action = self._optimal_policy.pi(s) + return self.q(s, optimal_action) diff --git a/nnabla_rl/models/mujoco/v_functions.py b/nnabla_rl/models/mujoco/v_functions.py index 8143a607..2c3414ca 100644 --- a/nnabla_rl/models/mujoco/v_functions.py +++ b/nnabla_rl/models/mujoco/v_functions.py @@ -155,3 +155,20 @@ def v(self, s: nn.Variable) -> nn.Variable: with nn.parameter_scope("linear3"): h = NPF.affine(h, n_outmaps=1, w_init=w_init) return h + + +class IQLVFunction(VFunction): + """VFunciton model proposed by Ilya Kostrikov in IQL paper for mujoco + environment. + + See: https://arxiv.org/pdf/2110.06169 + """ + + def v(self, s: nn.Variable) -> nn.Variable: + with nn.parameter_scope(self.scope_name): + h = NPF.affine(s, w_init=NI.OrthogonalInitializer(gain=np.sqrt(2.0)), n_outmaps=256, name="linear1") + h = NF.relu(x=h) + h = NPF.affine(h, w_init=NI.OrthogonalInitializer(gain=np.sqrt(2.0)), n_outmaps=256, name="linear2") + h = NF.relu(x=h) + h = NPF.affine(h, w_init=NI.OrthogonalInitializer(gain=np.sqrt(2.0)), n_outmaps=1, name="linear3") + return h diff --git a/reproductions/algorithms/mujoco/iql/.gitignore b/reproductions/algorithms/mujoco/iql/.gitignore new file mode 100644 index 00000000..e2b71361 --- /dev/null +++ b/reproductions/algorithms/mujoco/iql/.gitignore @@ -0,0 +1,2 @@ +*v2_results*/** +**/best*/** diff --git a/reproductions/algorithms/mujoco/iql/README.md b/reproductions/algorithms/mujoco/iql/README.md new file mode 100644 index 00000000..31ae7ec0 --- /dev/null +++ b/reproductions/algorithms/mujoco/iql/README.md @@ -0,0 +1,91 @@ +# Implicit Q-learning (IQL) reproduction (offline) + +This reproduction script trains the Implicit Q-learning (IQL) algorithm proposed by proposed by I. Kostrikov, et al. +in the paper: [OFFLINE REINFORCEMENT LEARNING WITH IMPLICIT Q-LEARNING](https://arxiv.org/abs/2110.06169) + +## Prerequisite + +Install d4rl + +``` +pip install git+https://github.com/rail-berkeley/d4rl@master#egg=d4rl +``` + +## How to run the reproduction script + +To run the reproduction script do + +```sh +python iql_reproduction.py +``` + +If you omit options, the script will run on halfcheetah-medium-expert-v2 environment with gpu id 0. + +You can change the training environment and gpu as follows + +```sh +python iql_reproduction.py --env --gpu +``` + +```sh +# Example: run the script on cpu and train the agent with walker2d-medium-v2: +$ python iql_reproduction.py --env walker2d-medium-v2 --gpu -1 +``` + +To check all available options type: + +```sh +python iql_reproduction.py --help +``` + +To check the trained result do + +```sh +python iql_reproduction.py --showcase --snapshot-dir --render +``` + +```sh +# Example: +$ python iql_reproduction.py --showcase --snapshot-dir ./halfcheetah-medium-expert-v2/seed-1/iteration-1/ --render +``` + +## Evaluation procedure + +We tested our implementation with 9 MuJoCo tasks also used in the [original paper](https://arxiv.org/abs/2110.06169) using 3 different initial random seeds: + +We evaluated the algorithm by running 10 trials in every 5000 iterations. + +## Result + +Mean of expert normalized scores across 3 seeds are as follows. + +|Env|nnabla-rl normalized score [%]|Reported normalized score [%] (IQL)| +|:---|:---:|:---:| +|halfcheetah-medium-v2|47.1|47.4| +|hopper-medium-v2|74.0|66.3| +|walker2d-medium-v2|83.0|78.3| +|halfcheetah-medium-replay-v2|44.5|44.2| +|hopper-medium-replay-v2|65.0|94.7| +|walker2d-medium-replay-v2|83.2|73.9| +|halfcheetah-medium-expert-v2|92.9|86.7| +|hopper-medium-expert-v2|97.4|91.5| +|walker2d-medium-expert-v2|110.4|109.6| + +## Learning curves + +### halfcheetah + +|medium|medium-replay|medium-expert| +|:---:|:---:|:---:| +|![halfcheetah-medium-v2 Result](reproduction_results/halfcheetah-medium-v2_results/result.png)|![halfcheetah-medium-replay-v2 Result](reproduction_results/halfcheetah-medium-replay-v2_results/result.png)|![halfcheetah-medium-expert-v2 Result](reproduction_results/halfcheetah-medium-expert-v2_results/result.png)| + +### hopper + +|medium|medium-replay|medium-expert| +|:---:|:---:|:---:| +|![hopper-medium-v2 Result](reproduction_results/hopper-medium-v2_results/result.png)|![hopper-medium-replay-v2 Result](reproduction_results/hopper-medium-replay-v2_results/result.png)|![hopper-medium-expert-v2 Result](reproduction_results/hopper-medium-expert-v2_results/result.png)| +### walker2d + +|medium|medium-replay|medium-expert| +|:---:|:---:|:---:| +|![walker2d-medium-v2 Result](reproduction_results/walker2d-medium-v2_results/result.png)|![walker2d-medium-replay-v2 Result](reproduction_results/walker2d-medium-replay-v2_results/result.png)|![walker2d-medium-expert-v2 Result](reproduction_results/walker2d-medium-expert-v2_results/result.png)| \ No newline at end of file diff --git a/reproductions/algorithms/mujoco/iql/compute_expert_normalized_score.py b/reproductions/algorithms/mujoco/iql/compute_expert_normalized_score.py new file mode 100755 index 00000000..5111dc47 --- /dev/null +++ b/reproductions/algorithms/mujoco/iql/compute_expert_normalized_score.py @@ -0,0 +1,131 @@ +# Copyright 2024 Sony Group Corporation. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import pathlib +import shutil +from csv import reader + +import d4rl +import numpy as np + +import nnabla_rl.writers as W + + +def load_histogram_data(path, dtype=float): + histogram = [] + with open(path) as f: + tsv_reader = reader(f, delimiter="\t") + for i, row in enumerate(tsv_reader): + if i == 0: + continue + histogram.append([row[0], np.asarray(row[1:])]) + return histogram + + +def rmdir_if_exists(path): + if path.exists(): + shutil.rmtree(path) + + +def list_all_directory_with(rootdir, filename): + directories = [] + for f in rootdir.iterdir(): + if f.is_file(): + if f.name in filename: + directories.append(f.parent) + if f.is_dir(): + directories.extend(list_all_directory_with(f, filename)) + return directories + + +def extract_iteration_num_and_returns(histogram_data): + iteration_nums = [] + returns = [] + for i in range(len(histogram_data)): + data_row = histogram_data[i] + if "returns" in data_row[0]: + iteration_nums.append(int(data_row[0].split(" ")[0])) + scores = data_row[1][0:].astype(float) + returns.append(scores) + + return iteration_nums, returns + + +def create_expert_normalized_score_file(histograms, file_outdir, d4rl_env_name): + iteration_nums = None + returns_list = None + for histogram in histograms: + data = load_histogram_data(histogram, dtype=str) + iteration_nums, returns = extract_iteration_num_and_returns(data) + if returns_list is None: + returns_list = returns + else: + for i, r in enumerate(returns): + returns_list[i] = np.concatenate([returns_list[i], r]) + + file_prefix = "expert_normalized_score.tsv" + writer = W.FileWriter(file_outdir, file_prefix=file_prefix) + for i, r in zip(iteration_nums, returns_list): + normalized_r = d4rl.get_normalized_score(d4rl_env_name, r) + mean = np.mean(normalized_r) * 100 + std_dev = np.std(normalized_r) * 100 + + scalar_results = {} + scalar_results["mean"] = mean + scalar_results["std_dev"] = std_dev + + writer.write_scalar(i, scalar_results) + + +def compile_results(args): + rootdir = pathlib.Path.cwd() + + histograms = {} + histogram_directories = list_all_directory_with(rootdir, args.eval_histogram_filename) + print(f"files: {histogram_directories}") + for directory in histogram_directories: + if args.resultdir not in str(directory): + continue + relative_dir = directory.relative_to(rootdir) + env_name = str(relative_dir).split("/")[1] + histogram_file = directory / args.eval_histogram_filename + print(f"found histogram file of env: {env_name} at: {histogram_file}") + if histogram_file.exists(): + if env_name in histograms.keys(): + histograms[env_name].append(histogram_file) + else: + histograms[env_name] = [histogram_file] + + for env_name, histograms in histograms.items(): + file_outdir = pathlib.Path(args.outdir) / pathlib.Path(env_name) + d4rl_env_name = env_name.replace("_results", "") + create_expert_normalized_score_file(histograms, file_outdir, d4rl_env_name) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--outdir", type=str, required=True, help="output directory") + parser.add_argument("--resultdir", type=str, required=True, help="result directory") + parser.add_argument( + "--eval-histogram-filename", + type=str, + default="evaluation_result_histogram.tsv", + help="eval result(histogram) filename", + ) + + args = parser.parse_args() + + compile_results(args) diff --git a/reproductions/algorithms/mujoco/iql/iql_reproduction.py b/reproductions/algorithms/mujoco/iql/iql_reproduction.py new file mode 100644 index 00000000..b7b408ea --- /dev/null +++ b/reproductions/algorithms/mujoco/iql/iql_reproduction.py @@ -0,0 +1,191 @@ +# Copyright 2024 Sony Group Corporation. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os + +import numpy as np +from tqdm import tqdm + +import nnabla.solvers as NS +import nnabla_rl.algorithms as A +import nnabla_rl.hooks as H +import nnabla_rl.writers as W +from nnabla.utils.learning_rate_scheduler import CosineScheduler +from nnabla_rl.builders import SolverBuilder +from nnabla_rl.environments.dummy import DummyMujocoEnv +from nnabla_rl.environments.environment_info import EnvironmentInfo +from nnabla_rl.replay_buffer import ReplayBuffer +from nnabla_rl.utils import serializers +from nnabla_rl.utils.evaluator import EpisodicEvaluator +from nnabla_rl.utils.reproductions import build_mujoco_env, d4rl_dataset_to_experiences, set_global_seed +from nnabla_rl.utils.solver_wrappers import AutoLearningRateScheduler + +try: + # import at the end. d4rl overrides logger unexpectedly + import d4rl # noqa +except ModuleNotFoundError: + # Ignore if d4rl is not installed + pass + + +class CosineDecayPolicySolverBuilder(SolverBuilder): + def __init__(self, max_iterations): + super().__init__() + self._max_iterations = max_iterations + + def build_solver(self, env_info, algorithm_config, **kwargs): + scheduler = CosineScheduler(algorithm_config.learning_rate, self._max_iterations) + solver = NS.Adam(alpha=algorithm_config.learning_rate) + solver = AutoLearningRateScheduler(solver, scheduler) + return solver + + +def build_env_and_dataset(env_name, seed=None): + d4rl_env = build_mujoco_env(env_name, seed=seed) + if env_name == "FakeMujocoNNablaRL-v1": + # Dummy environment + d4rl_dataset = d4rl_env.get_qlearning_dataset() + else: + d4rl_dataset = d4rl.qlearning_dataset(d4rl_env) + d4rl_dataset = clip_actions_in_dataset(d4rl_dataset) + d4rl_dataset = normalize_dataset_score(d4rl_dataset) + return d4rl_env, d4rl_dataset + + +def clip_actions_in_dataset(dataset, eps: float = 1e-5): + lim = 1.0 - eps + dataset["actions"] = np.clip(dataset["actions"], -lim, lim) + return dataset + + +def normalize_dataset_score(dataset): + def dataset_to_trajectories(dataset): + states = dataset["observations"] + actions = dataset["actions"] + rewards = dataset["rewards"] + terminals = dataset["terminals"] + next_states = dataset["next_observations"] + + def same_observation(obs1, obs2): + return np.linalg.norm(obs1 - obs2) <= 1e-6 + + trajectories = [] + trajectory = [] + for i in tqdm(range(len(states))): + trajectory.append((states[i], actions[i], rewards[i], terminals[i], next_states[i])) + if i == len(states) - 1: + continue + if terminals[i] == 1.0 or (not same_observation(states[i + 1], next_states[i])): + trajectories.append(trajectory) + trajectory = [] + if len(trajectory) != 0: + trajectories.append(trajectory) + assert len(trajectories) > 1 + return trajectories + + def max_min_returns(trajectories): + episode_returns = [] + for trajectory in trajectories: + total_return = 0 + for _, _, r, _, _ in trajectory: + total_return += r + episode_returns.append(total_return) + episode_returns.sort() + return episode_returns[-1], episode_returns[0] + + trajectories = dataset_to_trajectories(dataset) + max_return, min_return = max_min_returns(trajectories) + print(f"len trajectories: {len(trajectories)}") + print(f"max return: {max_return}, min return: {min_return}") + + dataset["rewards"] /= max_return - min_return + dataset["rewards"] *= 1000.0 + + return dataset + + +def run_training(args): + outdir = f"{args.env}_results/seed-{args.seed}" + if args.save_dir: + outdir = os.path.join(os.path.abspath(args.save_dir), outdir) + + eval_env, dataset = build_env_and_dataset(args.env, seed=args.seed) + dataset_size = dataset["observations"].shape[0] + experiences = d4rl_dataset_to_experiences(dataset, size=dataset_size) + buffer = ReplayBuffer(capacity=dataset_size) + buffer.append_all(experiences) + set_global_seed(args.seed) + + evaluator = EpisodicEvaluator(run_per_evaluation=10) + evaluation_hook = H.EvaluationHook( + eval_env, + evaluator, + timing=args.eval_timing, + writer=W.FileWriter(outdir=outdir, file_prefix="evaluation_result"), + ) + + save_snapshot_hook = H.SaveSnapshotHook(outdir, timing=args.save_timing) + iteration_num_hook = H.IterationNumHook(timing=100) + iteration_state_hook = H.IterationStateHook(timing=100) + + config = A.IQLConfig(gpu_id=args.gpu) + env_info = EnvironmentInfo.from_env(eval_env) + iql = A.IQL(env_info, config=config, policy_solver_builder=CosineDecayPolicySolverBuilder(args.total_iterations)) + + hooks = [save_snapshot_hook, evaluation_hook, iteration_num_hook, iteration_state_hook] + iql.set_hooks(hooks) + + iql.train_offline(buffer, total_iterations=args.total_iterations) + + eval_env.close() + + +def run_showcase(args): + if args.snapshot_dir is None: + raise ValueError("Please specify the snapshot dir for showcasing") + eval_env = build_mujoco_env(args.env, test=True, seed=args.seed + 200, render=args.render) + config = A.BEARConfig(gpu_id=args.gpu) + iql = serializers.load_snapshot(args.snapshot_dir, eval_env, algorithm_kwargs={"config": config}) + if not isinstance(iql, A.IQL): + raise ValueError("Loaded snapshot is not trained with IQL!") + + evaluator = EpisodicEvaluator(args.showcase_runs) + evaluator(iql, eval_env) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--env", type=str, default="halfcheetah-medium-expert-v2") + parser.add_argument("--gpu", type=int, default=0) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--render", action="store_true") + parser.add_argument("--showcase", action="store_true") + parser.add_argument("--snapshot-dir", type=str, default=None) + parser.add_argument("--save-dir", type=str, default=None) + parser.add_argument("--total_iterations", type=int, default=1000000) + parser.add_argument("--save_timing", type=int, default=5000) + parser.add_argument("--eval_timing", type=int, default=5000) + parser.add_argument("--showcase_runs", type=int, default=10) + + args = parser.parse_args() + + if args.showcase: + run_showcase(args) + else: + run_training(args) + + +if __name__ == "__main__": + main() diff --git a/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-expert-v2_results/evaluation_result_average_scalar.tsv b/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-expert-v2_results/evaluation_result_average_scalar.tsv new file mode 100644 index 00000000..90dac60d --- /dev/null +++ b/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-expert-v2_results/evaluation_result_average_scalar.tsv @@ -0,0 +1,201 @@ +iteration mean std_dev min max median +5000 1266.666 1355.399 -141.169 4400.218 732.209 +10000 1196.138 1593.788 -741.588 4892.609 719.542 +15000 2352.707 1372.822 -206.559 4847.543 2389.908 +20000 2650.388 1069.747 -309.990 4422.637 2711.249 +25000 2371.250 1064.325 -3.629 4083.050 2721.399 +30000 2666.967 1286.629 130.879 4750.373 2901.377 +35000 3166.319 1081.198 481.464 4872.898 3509.255 +40000 3053.740 1400.095 191.230 4910.179 3349.458 +45000 3152.421 1546.938 310.313 5310.430 3492.064 +50000 3397.273 1662.556 -488.405 5306.954 3982.990 +55000 4078.261 1234.827 875.401 5503.941 4559.578 +60000 3749.037 1496.393 478.077 5631.643 4504.217 +65000 4373.782 1162.236 1069.979 5525.547 4835.244 +70000 5161.543 803.000 2293.922 6466.076 5273.877 +75000 4350.992 1333.438 1622.995 6507.784 4547.760 +80000 5150.057 941.134 2219.358 6577.954 5298.891 +85000 4466.089 1550.098 510.560 6674.048 5010.778 +90000 4808.680 1258.759 323.462 6351.800 5135.706 +95000 5126.995 1540.319 744.281 6788.427 5816.183 +100000 5502.387 1451.748 2071.598 7620.754 5903.535 +105000 4511.703 2051.965 289.939 7586.142 4897.587 +110000 5162.327 1783.545 1369.356 7459.065 5900.293 +115000 5210.266 1622.145 1920.170 7494.386 5751.836 +120000 5916.794 1174.032 795.737 7394.356 6149.512 +125000 5756.378 1485.032 1779.918 8279.716 6016.178 +130000 5505.677 1565.970 1734.188 7511.701 5897.599 +135000 5852.169 1663.088 1889.806 8039.149 6380.261 +140000 5968.341 1396.848 1793.749 8151.315 6031.788 +145000 5425.443 1497.490 1573.692 7611.039 5910.899 +150000 5118.003 2392.410 115.024 7979.274 6087.448 +155000 5268.426 2315.694 518.711 8987.090 5516.608 +160000 6654.137 2053.869 1539.337 10133.473 7026.781 +165000 5046.936 2101.181 836.044 8194.596 5898.332 +170000 5690.684 2037.836 534.050 7814.272 6735.412 +175000 6235.226 1517.289 2351.213 8592.832 6344.168 +180000 6615.969 2147.968 1682.707 9706.067 7203.040 +185000 6497.997 2638.154 458.342 10064.346 7632.492 +190000 5735.202 2549.434 1016.852 9483.488 6567.099 +195000 7124.796 2330.995 740.508 9927.459 7515.655 +200000 6572.282 2307.872 2318.459 10132.596 6613.796 +205000 7182.438 2034.795 2346.269 10263.840 7747.048 +210000 6283.549 2065.517 1669.448 9678.188 6613.994 +215000 7609.831 2339.107 1590.613 11149.298 7953.249 +220000 7317.973 2464.438 1860.448 10663.238 7818.834 +225000 7840.808 2270.125 2547.346 10616.209 8355.158 +230000 7105.595 2931.862 1017.854 10791.449 8145.422 +235000 7063.199 2030.745 2634.425 9352.318 7659.114 +240000 6906.708 2175.577 1809.401 9633.977 7494.845 +245000 7059.779 2681.843 1253.528 10309.907 7615.231 +250000 7570.640 2854.331 980.698 10980.686 8925.968 +255000 6982.143 2394.101 1867.087 9869.111 7681.628 +260000 8284.352 1983.463 2510.525 11055.254 8652.904 +265000 8671.507 2285.976 1773.019 10823.261 9614.447 +270000 8549.557 2064.559 3115.888 10582.027 9303.233 +275000 7211.643 2734.336 708.147 11133.695 8054.450 +280000 8829.908 1995.695 1628.004 11022.795 9199.574 +285000 8096.261 1979.767 2985.227 10758.041 8710.645 +290000 8233.430 2814.772 3051.631 11168.555 9637.162 +295000 8487.518 2365.571 1603.412 11073.799 9398.728 +300000 7932.932 2534.643 1819.776 10602.559 8873.917 +305000 7910.333 2733.800 2058.276 11080.236 9185.605 +310000 8562.029 2171.786 1660.555 10979.670 9059.278 +315000 8847.555 2460.658 2322.830 11165.709 9751.300 +320000 8796.222 2654.942 1230.020 11229.412 9875.025 +325000 8613.410 2849.399 2278.397 11145.623 9850.584 +330000 9306.372 2189.548 3134.597 10954.233 10175.425 +335000 8724.491 2259.828 2472.926 11395.834 9336.755 +340000 9139.608 2068.080 1331.394 11057.590 9892.556 +345000 8466.764 2497.486 2070.572 11073.980 9863.525 +350000 9174.208 1549.195 4949.110 11191.070 9578.595 +355000 10094.412 1547.445 4417.189 11181.732 10504.762 +360000 9076.962 2863.089 1887.464 11291.204 10611.461 +365000 8704.365 2773.528 2345.177 11232.244 9676.025 +370000 9494.878 1426.067 5426.192 10901.411 9949.575 +375000 9468.268 1936.470 3497.299 11088.642 10142.667 +380000 8237.907 2969.561 1873.837 10844.180 9792.786 +385000 8808.345 2244.110 2459.805 11111.924 9738.838 +390000 8432.417 2841.889 476.266 11218.435 9557.707 +395000 10314.122 1532.348 2364.974 11146.278 10730.744 +400000 9258.524 2276.656 3468.893 11396.502 10259.418 +405000 9166.625 2476.964 1261.461 11062.303 10327.862 +410000 8400.489 2858.738 2121.776 11070.535 9883.912 +415000 9819.791 2226.652 2213.432 11268.742 10593.654 +420000 10485.352 1285.951 4142.143 11310.890 10881.633 +425000 9336.176 2525.887 1656.354 11514.196 10352.303 +430000 9220.527 2425.237 2644.723 11270.755 10115.365 +435000 10359.642 1349.061 4106.802 11266.475 10670.533 +440000 10652.906 546.424 9141.305 11306.537 10841.818 +445000 9184.091 2697.579 1837.683 11025.602 10459.219 +450000 9017.450 2729.947 2267.674 11299.537 10410.537 +455000 9282.392 2420.183 826.221 11372.820 10238.162 +460000 9558.433 2321.176 2207.031 11220.853 10679.922 +465000 10755.651 474.256 9472.158 11303.566 10871.642 +470000 9722.565 2032.960 3936.050 11158.785 10537.374 +475000 10393.559 1211.693 4631.583 11328.693 10806.575 +480000 9652.567 2210.840 2316.767 11194.270 10377.503 +485000 10082.966 2213.527 2601.187 11614.978 10804.102 +490000 10222.450 1238.582 5058.019 11116.446 10624.389 +495000 9421.845 2605.937 2343.035 11310.685 10601.412 +500000 9768.817 2184.310 3590.794 11383.548 10725.865 +505000 10051.385 1999.976 1989.651 11377.143 10425.444 +510000 9906.865 1918.860 3396.762 11048.725 10605.706 +515000 10513.173 1025.194 5309.012 11147.229 10735.040 +520000 10264.686 1790.201 2648.947 11761.795 10851.152 +525000 9772.124 2130.024 1320.476 11344.542 10365.081 +530000 10801.560 568.244 8678.711 11610.693 10897.675 +535000 10079.823 1971.285 1441.749 11099.527 10777.520 +540000 10637.516 956.543 7134.873 11270.887 10872.523 +545000 10178.285 1766.442 1812.510 11240.975 10779.034 +550000 9564.626 2375.967 2235.460 11401.283 10442.906 +555000 9971.951 1936.020 1848.092 11148.044 10623.254 +560000 10205.553 1798.443 2887.220 11441.104 10866.380 +565000 10698.437 761.107 7211.668 11321.073 10908.724 +570000 10203.635 1619.260 2754.260 11578.322 10615.342 +575000 10513.131 1582.672 4555.421 11357.612 10929.187 +580000 10556.916 1207.923 5790.000 11579.693 10899.364 +585000 10559.413 1370.342 4567.198 11420.493 10880.832 +590000 10649.358 1144.263 5209.242 11494.702 10973.981 +595000 9276.283 2815.665 1861.373 11330.500 10584.572 +600000 10798.220 648.828 8234.113 11537.494 10968.192 +605000 10854.309 681.687 7787.009 11587.533 11015.704 +610000 10378.759 1605.898 2661.783 11501.371 10766.412 +615000 10123.217 1733.821 2452.990 11300.984 10856.162 +620000 10289.349 2150.473 3339.584 11584.975 11084.142 +625000 10659.960 1100.231 7000.027 11439.333 11097.481 +630000 10385.419 1679.505 3215.156 11404.765 10815.157 +635000 10522.949 1554.082 4471.082 11383.246 11121.505 +640000 10550.959 1523.538 5094.854 11525.221 11054.118 +645000 10391.058 1590.165 2487.499 11545.443 10801.897 +650000 10964.020 1102.292 5137.221 11444.816 11159.862 +655000 10526.035 1839.133 3614.547 11442.485 11113.701 +660000 10889.043 840.506 6613.884 11407.653 11065.076 +665000 10778.788 1463.896 3022.254 11562.139 11081.568 +670000 10794.100 1231.620 4517.185 11422.086 11097.548 +675000 10748.225 1171.813 4809.042 11400.578 11010.448 +680000 10714.993 1591.483 4592.495 11432.666 11131.764 +685000 10862.327 808.915 7039.540 11604.828 11074.358 +690000 11022.597 332.587 9858.575 11346.707 11154.186 +695000 10586.837 1788.024 2799.126 11618.301 11031.718 +700000 10989.546 654.953 8023.929 11542.762 11117.116 +705000 10943.172 771.604 7050.737 11533.684 11089.323 +710000 11033.850 834.364 6641.839 11482.398 11149.415 +715000 11035.831 206.828 10268.152 11327.202 11077.608 +720000 11092.912 325.517 10013.609 11561.975 11154.199 +725000 10902.219 1334.531 3811.153 11545.076 11191.796 +730000 11036.914 330.693 10141.632 11546.016 11073.998 +735000 10926.955 1277.144 4174.796 11762.855 11126.771 +740000 11140.852 291.408 10295.154 11569.330 11214.537 +745000 10842.331 1448.576 3320.854 11579.185 11145.549 +750000 10360.965 1748.036 4016.880 11460.066 11067.852 +755000 11202.387 193.848 10680.247 11551.117 11226.945 +760000 11126.984 209.778 10727.403 11480.338 11159.243 +765000 11135.739 139.257 10843.691 11437.111 11104.272 +770000 11164.733 397.594 9209.146 11587.023 11216.202 +775000 10630.449 1828.758 2281.259 11536.060 11156.807 +780000 10672.939 1442.924 4022.076 11627.822 11107.609 +785000 11191.287 223.610 10415.339 11486.213 11192.259 +790000 11042.070 522.055 8708.943 11593.941 11179.399 +795000 11228.668 273.180 10076.760 11518.580 11296.318 +800000 11043.655 424.562 9788.688 11551.990 11230.353 +805000 11083.505 236.888 10572.963 11422.417 11159.395 +810000 11222.787 416.897 9134.381 11528.406 11279.037 +815000 11190.412 297.779 10239.486 11543.072 11289.904 +820000 10995.378 694.051 8519.854 11553.379 11183.489 +825000 11154.975 245.910 10634.102 11602.396 11193.323 +830000 11252.205 192.926 10556.119 11585.154 11279.411 +835000 10659.235 1801.495 2973.437 11399.012 11151.790 +840000 11086.124 253.509 10426.143 11413.259 11129.131 +845000 11217.403 190.827 10539.610 11515.824 11212.761 +850000 11039.626 1203.074 4648.105 11533.744 11297.923 +855000 11186.446 242.786 10485.200 11485.924 11235.571 +860000 11127.731 393.372 9826.391 11494.234 11256.174 +865000 11234.951 255.379 10619.771 11634.050 11295.037 +870000 10503.397 2222.847 2197.923 11436.855 11185.861 +875000 10994.323 712.989 7671.539 11512.430 11204.162 +880000 11212.327 256.194 10346.703 11491.920 11277.809 +885000 11167.265 229.234 10577.008 11552.713 11214.336 +890000 11156.819 279.538 10305.417 11514.128 11237.542 +895000 11106.107 255.493 10236.299 11442.561 11129.114 +900000 11187.127 231.913 10600.496 11522.893 11191.514 +905000 11084.568 453.933 9428.812 11502.766 11216.289 +910000 11034.414 1314.931 4003.578 11567.858 11313.283 +915000 11204.360 173.702 10860.802 11584.713 11249.086 +920000 11206.190 245.176 10401.227 11565.926 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a/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-expert-v2_results/expert_normalized_score.tsv_scalar.tsv b/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-expert-v2_results/expert_normalized_score.tsv_scalar.tsv new file mode 100644 index 00000000..f12de8a4 --- /dev/null +++ b/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-expert-v2_results/expert_normalized_score.tsv_scalar.tsv @@ -0,0 +1,201 @@ +iteration mean std_dev +5000 12.459 10.917 +10000 11.891 12.837 +15000 21.207 11.058 +20000 23.605 8.616 +25000 21.356 8.573 +30000 23.738 10.363 +35000 27.760 8.709 +40000 26.854 11.277 +45000 27.648 12.460 +50000 29.621 13.391 +55000 35.106 9.946 +60000 32.454 12.053 +65000 37.486 9.361 +70000 43.831 6.468 +75000 37.302 10.740 +80000 43.739 7.581 +85000 38.230 12.486 +90000 40.989 10.139 +95000 43.553 12.407 +100000 46.577 11.693 +105000 38.597 16.528 +110000 43.838 14.366 +115000 44.224 13.066 +120000 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a/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-expert-v2_results/seed-1/evaluation_result_scalar.tsv b/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-expert-v2_results/seed-1/evaluation_result_scalar.tsv new file mode 100644 index 00000000..12659cb5 --- /dev/null +++ b/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-expert-v2_results/seed-1/evaluation_result_scalar.tsv @@ -0,0 +1,201 @@ +iteration mean std_dev min max median +5000 1698.253 1506.101 38.844 4400.218 1469.406 +10000 702.622 1175.966 -741.588 2330.669 456.978 +15000 2371.308 1486.462 -206.559 4847.543 2387.646 +20000 1786.644 994.895 -309.990 3680.819 1763.024 +25000 2877.028 910.568 1117.406 4083.050 3157.075 +30000 3229.387 1236.476 448.482 4657.185 3218.556 +35000 3257.591 989.792 481.464 4184.915 3332.040 +40000 2043.586 1208.857 191.230 4105.009 2002.185 +45000 4062.398 1531.490 418.196 5310.430 4600.210 +50000 3138.456 1478.338 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b/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-expert-v2_results/seed-10/best_score_scalar.tsv @@ -0,0 +1,2 @@ +iteration best_mean_iteration mean std_dev best_median_iteration median +1 860000.000 11274.864 96.789 980000.000 11318.381 diff --git a/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-expert-v2_results/seed-10/evaluation_result_histogram.tsv b/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-expert-v2_results/seed-10/evaluation_result_histogram.tsv new file mode 100644 index 00000000..9790cdd5 --- /dev/null +++ b/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-expert-v2_results/seed-10/evaluation_result_histogram.tsv @@ -0,0 +1,201 @@ +iteration(key) values +5000 (returns) 773.325 -69.627 513.030 1349.853 364.313 2462.145 3113.190 -141.169 567.666 -112.762 +10000 (returns) 652.017 743.865 2881.414 4892.609 -568.023 695.219 3422.670 -672.177 -461.701 3283.648 +15000 (returns) 1067.736 3385.233 4072.147 3719.298 4019.497 2058.006 3099.941 105.877 3873.336 3353.202 +20000 (returns) 2818.697 1239.602 2056.091 2054.077 2252.167 2721.474 2842.570 3681.614 2310.987 2956.506 +25000 (returns) 1598.253 3902.726 -3.629 1770.044 1792.849 3371.183 822.605 2665.083 2777.716 430.402 +30000 (returns) 1628.844 3191.123 3202.249 3584.782 4750.373 3015.889 3427.229 3920.813 3659.994 2028.303 +35000 (returns) 3905.801 3075.088 3750.044 666.277 4872.898 507.343 2896.808 3885.966 1568.581 3624.217 +40000 (returns) 1573.772 4411.857 4119.718 4724.975 3616.443 4751.939 3049.513 4602.074 4262.893 4519.851 +45000 (returns) 3163.823 3747.160 2272.767 977.710 3417.671 2110.264 2831.258 4033.555 3566.458 787.576 +50000 (returns) 5271.799 3486.685 4733.750 724.636 818.263 5176.559 4549.560 2250.363 578.882 5306.954 +55000 (returns) 4124.892 5103.933 1600.562 4552.772 5053.261 3920.687 3957.882 4754.117 5077.621 3371.786 +60000 (returns) 4596.538 2842.665 2874.651 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b/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-v2_results/seed-100/best_score_scalar.tsv @@ -0,0 +1,2 @@ +iteration best_mean_iteration mean std_dev best_median_iteration median +1 250000.000 5603.554 63.315 250000.000 5613.347 diff --git a/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-v2_results/seed-100/evaluation_result_histogram.tsv b/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-v2_results/seed-100/evaluation_result_histogram.tsv new file mode 100644 index 00000000..1eb4f14d --- /dev/null +++ b/reproductions/algorithms/mujoco/iql/reproduction_results/halfcheetah-medium-v2_results/seed-100/evaluation_result_histogram.tsv @@ -0,0 +1,201 @@ +iteration(key) values +5000 (returns) -436.801 2544.024 1141.958 3019.746 -128.521 528.420 1757.210 200.141 678.318 3324.401 +10000 (returns) 3065.559 516.523 -289.453 929.379 93.001 -419.608 4474.328 3067.193 3885.526 2822.966 +15000 (returns) 714.010 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5502.439 5349.451 5462.274 5363.900 5269.307 5466.209 5408.095 +205000 (returns) 5490.154 5555.975 5398.140 5520.111 5357.489 5567.993 5553.452 5450.333 5428.999 5264.311 +210000 (returns) 5454.811 5443.930 5379.209 5634.847 5305.854 5549.140 5401.983 5551.110 5517.074 5548.571 +215000 (returns) 5492.731 5543.605 5428.488 5516.104 5569.527 5426.224 5494.019 5495.886 5496.800 5481.451 +220000 (returns) 5477.599 5342.469 5333.369 5463.643 5547.989 5559.884 5405.293 5294.914 5503.017 5419.603 +225000 (returns) 5605.311 5426.662 5401.653 5516.725 5490.119 5276.032 5452.817 5648.192 5327.906 5591.045 +230000 (returns) 5507.825 5545.793 5513.794 5391.981 5434.143 5411.378 5345.953 5494.102 5356.629 5532.206 +235000 (returns) 5451.680 5303.920 5513.578 5496.093 5566.672 5535.745 5505.566 5357.547 5372.476 5264.953 +240000 (returns) 5504.636 5512.688 5560.753 5666.707 5321.768 5535.062 5674.213 5540.825 5659.165 5607.190 +245000 (returns) 5579.564 5389.661 5338.638 5616.634 5341.322 5515.642 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1534.660 +210000 1454.809 102.295 1305.634 1602.275 1463.825 +215000 2163.383 574.306 1372.476 3289.541 1900.539 +220000 1970.240 555.412 1595.668 3277.393 1726.402 +225000 1678.823 300.201 1356.791 2426.652 1620.014 +230000 1634.441 199.134 1394.622 2182.184 1626.163 +235000 2331.222 657.081 1466.312 3266.326 2319.021 +240000 1908.657 570.115 1498.491 3372.910 1590.860 +245000 1578.903 84.023 1449.678 1784.729 1572.514 +250000 2392.157 707.008 1457.428 3359.764 2163.320 +255000 2397.624 515.107 1872.062 3288.739 2336.214 +260000 1990.938 539.257 1355.398 3092.104 1767.866 +265000 2034.352 558.075 1529.384 3104.405 1877.099 +270000 1873.507 559.370 1453.509 3309.202 1586.046 +275000 2050.249 584.934 1390.562 3309.106 1875.110 +280000 2369.064 551.558 1600.050 3248.684 2250.206 +285000 1533.003 217.486 1291.890 1877.396 1489.776 +290000 2022.441 376.556 1485.946 2779.956 1898.486 +295000 1781.410 402.045 1444.379 2810.906 1589.089 +300000 1820.895 405.988 1492.193 2884.097 1618.406 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508.702 1884.908 3261.256 2576.271 +500000 2550.423 512.925 1913.975 3301.834 2468.278 +505000 2225.496 551.886 1620.994 3268.128 2010.938 +510000 2175.648 444.229 1835.928 3243.429 1923.643 +515000 1972.114 441.715 1569.456 3115.653 1912.720 +520000 2202.784 520.097 1595.814 3315.935 1980.222 +525000 2340.901 598.145 1760.239 3249.476 2053.417 +530000 2242.485 517.773 1819.328 3251.913 2038.970 +535000 1990.283 295.277 1675.578 2791.785 1885.673 +540000 2091.087 285.891 1780.746 2695.158 1934.756 +545000 2404.226 521.124 1876.915 3257.709 2301.689 +550000 2448.665 546.578 1871.866 3259.621 2178.332 +555000 2289.539 368.869 1835.594 3181.326 2221.416 +560000 2740.019 540.842 1835.013 3256.080 2951.273 +565000 2311.802 406.709 1795.192 3250.841 2225.713 +570000 1851.533 177.547 1590.924 2167.124 1806.319 +575000 1937.673 81.379 1794.733 2095.215 1934.099 +580000 2546.896 552.362 1932.781 3366.329 2334.205 +585000 2705.633 513.385 1951.412 3278.827 2691.340 +590000 2073.905 467.204 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2968.423 1971.921 +690000 2118.694 404.976 1639.779 3064.368 1952.845 +695000 2420.320 586.292 1779.621 3302.593 2190.708 +700000 1969.043 142.936 1780.397 2176.698 1897.764 +705000 2164.697 371.419 1730.055 2884.701 1997.957 +710000 2399.721 493.821 1895.250 3260.482 2195.702 +715000 2149.740 517.405 1817.201 3269.846 1907.047 +720000 2262.813 494.365 1888.719 3311.441 2102.141 +725000 2343.435 643.503 1626.433 3364.276 2042.721 +730000 1984.333 295.518 1532.142 2648.539 1908.676 +735000 2121.767 338.558 1789.085 2792.312 1915.336 +740000 2042.814 246.118 1808.666 2591.634 1918.039 +745000 1923.341 256.353 1607.784 2505.843 1850.296 +750000 2373.598 620.491 1679.567 3292.088 2141.097 +755000 2132.308 309.730 1813.949 2698.759 1963.171 +760000 2238.396 446.379 1791.641 3346.014 2141.900 +765000 2315.671 506.354 1720.166 3355.667 2270.216 +770000 2406.400 544.921 1817.055 3366.909 2195.027 +775000 2286.023 481.009 1776.608 3362.740 2223.805 +780000 2304.655 410.360 1810.601 3023.235 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15.358 450000.000 5123.772 diff --git a/reproductions/algorithms/mujoco/iql/reproduction_results/walker2d-medium-expert-v2_results/seed-1/evaluation_result_histogram.tsv b/reproductions/algorithms/mujoco/iql/reproduction_results/walker2d-medium-expert-v2_results/seed-1/evaluation_result_histogram.tsv new file mode 100644 index 00000000..9f5888af --- /dev/null +++ b/reproductions/algorithms/mujoco/iql/reproduction_results/walker2d-medium-expert-v2_results/seed-1/evaluation_result_histogram.tsv @@ -0,0 +1,201 @@ +iteration(key) values +5000 (returns) 610.153 637.771 632.080 539.036 650.206 503.382 566.821 593.426 635.065 516.471 +10000 (returns) 15.519 17.123 17.817 25.448 24.765 18.630 19.566 15.788 12.851 15.881 +15000 (returns) 4286.456 2438.527 478.569 4933.980 3494.995 3420.310 1510.962 4441.971 1187.227 1326.257 +20000 (returns) 2266.558 1891.715 2482.497 3596.519 2059.619 3522.474 3171.641 2886.590 4912.884 2051.102 +25000 (returns) 1878.003 3746.110 2144.328 1275.823 2652.290 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2092.927 1749.855 2101.821 +80000 (returns) 2302.534 2800.984 1978.338 2098.069 2544.430 2245.685 2003.419 2247.287 1833.107 1510.571 +85000 (returns) 2190.772 2198.175 2150.627 3826.646 1812.409 1840.134 2271.131 2006.804 2146.145 3054.370 +90000 (returns) 1604.494 3057.047 4395.375 2767.829 3190.712 1448.249 2338.296 2407.927 2126.538 2481.351 +95000 (returns) 1825.875 1003.357 2283.955 2441.197 2745.042 1521.989 3665.842 1684.083 2040.136 2061.704 +100000 (returns) 2899.546 2836.988 2804.450 3923.894 2493.165 2931.898 1797.511 2910.623 2810.680 4048.391 +105000 (returns) 1614.352 1569.691 2081.733 1987.039 1422.330 1247.270 1913.053 1416.965 1999.021 1602.252 +110000 (returns) 1870.499 2278.741 2075.644 1738.743 2261.927 2536.376 2547.700 2760.430 2454.116 2993.042 +115000 (returns) 2883.049 3618.379 2808.390 3776.998 3082.153 3764.734 1964.446 2216.154 3184.458 3758.085 +120000 (returns) 997.073 2816.602 2684.471 1004.207 2094.409 2606.797 999.254 2070.391 2301.328 2563.236 +125000 (returns) 2202.953 2545.327 2578.876 2236.400 3065.756 2227.580 2782.932 2268.168 2707.030 2268.715 +130000 (returns) 3753.972 2685.653 1854.230 2228.180 2231.835 2569.759 3803.106 1847.162 1850.590 4309.383 +135000 (returns) 3590.564 2216.975 2374.963 1751.291 3519.221 2922.502 4056.491 3818.261 2227.364 2768.415 +140000 (returns) 2994.922 2746.091 2764.698 2566.942 2317.462 2182.030 2395.189 2277.851 3870.548 2570.995 +145000 (returns) 4045.686 3802.493 2294.939 2803.888 2436.346 3687.104 3627.165 3944.147 4119.691 3029.301 +150000 (returns) 1877.873 2091.393 2522.840 3215.406 3088.603 1480.092 2466.218 2426.985 2212.324 2318.233 +155000 (returns) 3344.254 4046.271 1224.627 2132.194 4279.396 2049.558 2141.495 4103.821 1621.818 2396.090 +160000 (returns) 2941.137 2734.759 2061.417 3930.302 3785.653 3115.876 3795.490 3912.496 3562.529 2478.060 +165000 (returns) 2458.356 2262.691 1981.424 2090.462 3072.013 2110.409 3739.370 2021.198 2117.229 2891.665 +170000 (returns) 4090.756 4251.049 4160.838 3924.321 3938.462 4049.775 3945.593 4220.206 4244.135 2357.335 +175000 (returns) 2219.752 3227.179 3642.560 2237.242 4101.598 2664.706 2542.789 4177.825 2338.014 2644.116 +180000 (returns) 3622.827 3726.280 2987.966 3855.343 4038.891 2121.419 2293.949 4079.272 3844.568 4009.392 +185000 (returns) 2442.469 1755.670 3619.475 4103.501 4101.307 2801.117 3077.332 2787.183 2539.907 3864.262 +190000 (returns) 1022.762 2588.319 1725.390 3136.064 3017.421 3397.534 2594.052 1005.357 1141.184 2319.425 +195000 (returns) 3943.373 2385.551 2852.051 3205.761 3048.159 3642.441 3946.207 2258.150 1118.991 3978.650 +200000 (returns) 3786.340 2432.655 3098.360 2715.120 3892.681 3993.999 2195.626 3850.098 4044.189 1548.987 +205000 (returns) 2794.997 2721.794 2406.133 2287.441 4016.194 1951.271 2795.482 1755.890 4313.515 3210.906 +210000 (returns) 1820.512 2775.170 2249.987 2564.112 4275.897 3300.316 1315.167 2112.116 3150.113 2420.907 +215000 (returns) 4066.388 3730.151 4084.477 2357.898 3798.023 3855.973 3728.314 3857.764 3769.234 4027.626 +220000 (returns) 3852.743 1969.856 4097.987 4125.094 3941.632 3923.815 3819.542 2884.412 3268.291 4038.463 +225000 (returns) 4039.630 4114.881 3251.471 4307.979 2965.104 2441.372 2751.952 3614.412 4099.436 2062.035 +230000 (returns) 2321.592 3738.056 3794.481 3562.317 2622.073 3730.946 2625.604 3670.630 3841.865 2676.345 +235000 (returns) 3573.479 3665.681 3812.357 1416.131 3334.364 3801.142 4088.755 4156.171 3853.227 4018.824 +240000 (returns) 3918.445 3913.080 3807.746 3833.863 3151.880 3206.645 3582.919 2435.742 3908.098 3656.085 +245000 (returns) 2420.651 3196.295 2106.181 4006.692 1843.982 1865.184 4198.927 3104.876 3294.512 3846.523 +250000 (returns) 3322.919 2191.890 3936.328 3846.963 3656.144 3834.711 2518.212 2484.490 2886.041 3678.799 +255000 (returns) 3762.561 3440.782 3779.731 2741.620 3565.976 4025.199 2895.357 3794.204 3766.176 3866.521 +260000 (returns) 2468.601 4097.331 4118.725 4210.074 3993.881 3545.182 3901.719 2689.544 4082.240 3978.225 +265000 (returns) 2660.214 2023.482 3043.074 1769.551 2006.297 3841.648 3946.995 3761.214 3438.275 1711.260 +270000 (returns) 2936.695 1737.098 3987.635 4079.895 3982.935 2904.210 2328.728 3941.615 3956.863 3714.067 +275000 (returns) 4003.468 4170.896 4051.707 4154.107 2268.509 2069.828 4009.755 2529.223 2349.786 2339.759 +280000 (returns) 549.932 2096.437 2481.222 3954.168 3509.781 3307.862 3904.062 4042.222 3925.422 3904.109 +285000 (returns) 4086.234 4196.982 2629.370 3535.218 2666.751 3139.620 3029.434 4258.561 4162.727 2336.705 +290000 (returns) 3034.659 380.798 1547.115 4001.399 4074.909 1814.768 2466.258 3974.004 2591.844 4013.100 +295000 (returns) 3024.824 2839.980 4347.615 2056.385 386.267 4273.690 3833.983 2455.212 3858.080 2376.159 +300000 (returns) 3589.478 3509.126 3552.105 3740.859 3835.403 3560.947 3779.222 3524.659 3448.178 3195.603 +305000 (returns) 3217.632 3938.449 3926.155 4074.775 3769.778 3101.982 1811.527 2005.745 1983.493 1997.293 +310000 (returns) 3925.668 3893.759 4120.243 3841.141 1607.989 3188.241 4000.886 4175.493 3733.958 4126.008 +315000 (returns) 3738.374 3743.716 3572.992 3771.335 4118.839 3867.809 3784.740 3715.474 3732.524 3672.950 +320000 (returns) 3575.336 3846.177 3915.397 3645.784 3820.995 3598.390 3683.355 3981.538 3619.458 3663.680 +325000 (returns) 1750.926 2796.638 2957.131 2008.085 2556.934 2245.155 4000.503 3388.685 2920.065 3944.836 +330000 (returns) 4174.159 3706.796 2613.121 3724.640 1746.613 3782.303 3880.487 3972.976 4095.541 3964.931 +335000 (returns) 3681.988 3809.386 3689.201 3660.545 3719.731 3803.056 470.862 3716.772 3656.449 3736.136 +340000 (returns) 3914.686 2133.351 3122.519 4200.854 4127.841 3897.580 3680.413 3867.211 2658.786 4093.458 +345000 (returns) 3936.651 3792.827 2882.349 4159.070 3713.800 4063.767 3741.991 4045.607 4226.530 3807.438 +350000 (returns) 3919.247 3839.035 3632.203 2738.839 2373.885 3301.348 2471.189 3874.076 3540.257 2908.741 +355000 (returns) 3290.860 3899.093 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2130.530 3980.048 3769.132 3791.113 3779.328 +405000 (returns) 2323.727 4090.221 4110.799 1568.187 4332.264 4038.214 4285.451 4165.880 4287.733 2824.494 +410000 (returns) 3189.080 1941.866 4048.722 1987.704 2937.196 1838.097 2562.977 3828.391 1128.129 4294.896 +415000 (returns) 3890.290 3999.631 3844.359 3917.101 3804.006 4155.172 3882.682 3692.376 2930.711 3743.948 +420000 (returns) 3400.325 3715.687 2671.689 4197.200 4012.621 4219.092 4228.572 2937.248 4066.961 3489.589 +425000 (returns) 3486.647 3866.129 3632.720 944.079 3773.217 1244.279 2482.071 3872.776 788.020 3696.941 +430000 (returns) 2711.507 2791.509 3746.053 2329.789 3363.680 2117.508 3889.550 2371.794 4233.972 2346.375 +435000 (returns) 3694.120 3681.439 3599.560 3673.369 3469.553 3855.129 3607.500 3705.167 3510.557 3610.841 +440000 (returns) 3608.226 3298.433 3656.239 3645.608 3758.617 3697.327 3564.023 3684.869 3675.991 3172.866 +445000 (returns) 3680.638 3730.623 3875.695 3742.258 3608.660 3770.661 3950.022 3901.957 3688.269 3595.799 +450000 (returns) 4016.169 3805.685 3209.382 4332.438 3715.842 2927.643 2873.680 3744.385 3846.956 3783.391 +455000 (returns) 3980.968 3998.810 3932.145 4052.865 3812.081 3851.547 3805.771 4064.184 3802.022 3834.345 +460000 (returns) 2594.916 590.771 537.130 2551.562 2631.841 4145.271 3925.986 2356.716 3773.509 1510.918 +465000 (returns) 4047.191 3029.155 4185.321 3272.860 4004.246 3279.597 3774.118 4184.617 3825.954 3540.304 +470000 (returns) 4138.764 2919.457 4014.321 4053.531 3961.912 2916.652 3833.096 2616.424 2468.201 1930.015 +475000 (returns) 3911.478 2751.718 3137.775 4099.316 3837.286 1842.544 2413.108 3772.431 2480.541 2933.802 +480000 (returns) 4060.724 3299.805 4070.865 3625.186 3925.880 3922.635 3642.958 3603.303 4132.887 4215.883 +485000 (returns) 4097.690 4083.631 3741.546 3854.428 3651.148 3709.799 3541.271 3772.273 3772.197 3743.335 +490000 (returns) 3941.793 3773.366 2957.498 4224.744 4167.396 4142.959 2635.931 3951.692 4150.554 2299.782 +495000 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b/reproductions/algorithms/mujoco/iql/reproduction_results/walker2d-medium-replay-v2_results/seed-10/evaluation_result_histogram.tsv @@ -0,0 +1,201 @@ +iteration(key) values +5000 (returns) 244.604 251.818 240.187 252.039 246.543 255.651 239.150 251.984 237.545 236.353 +10000 (returns) 289.915 302.153 287.547 282.932 304.922 286.730 287.140 288.538 287.976 296.110 +15000 (returns) 604.395 260.291 277.480 281.632 540.015 634.322 528.761 285.039 533.395 638.962 +20000 (returns) 141.402 115.035 127.305 305.611 130.220 118.594 124.114 136.805 120.356 122.936 +25000 (returns) 862.290 686.440 865.649 895.741 712.276 889.045 787.197 962.292 1193.747 1179.417 +30000 (returns) 473.323 328.041 672.536 362.661 336.845 596.782 611.313 338.561 847.900 349.350 +35000 (returns) 731.638 828.083 689.945 733.457 759.965 704.325 314.875 722.129 706.403 713.207 +40000 (returns) 1551.367 522.996 1176.975 1668.368 1662.461 382.940 390.382 1934.143 543.870 379.284 +45000 (returns) 860.851 864.664 1286.452 909.506 766.753 957.734 882.986 868.114 966.896 892.336 +50000 (returns) 434.747 1218.913 1942.594 2309.125 301.629 642.925 834.659 752.925 1854.259 825.339 +55000 (returns) 517.523 1378.510 528.390 1385.355 581.002 530.530 539.377 1382.734 513.665 1358.781 +60000 (returns) 1681.342 1519.469 1667.140 1584.786 1466.676 1457.275 1654.660 1774.975 2133.107 1543.393 +65000 (returns) 2636.408 2493.459 695.470 3768.643 3943.825 826.112 3001.785 669.534 305.238 2795.851 +70000 (returns) 1783.085 1486.672 2156.704 1807.997 2049.899 1794.627 1761.336 1861.366 1914.329 1913.464 +75000 (returns) 1803.229 2023.812 1895.901 2728.022 1700.654 3023.252 2673.345 2202.041 1301.838 1710.853 +80000 (returns) 3241.467 3914.279 3926.719 3853.737 3660.100 3846.375 4001.468 2151.076 3901.535 3216.593 +85000 (returns) 1904.843 1542.228 2278.184 2003.803 3401.855 2276.534 1541.310 2503.801 2150.563 2399.574 +90000 (returns) 1146.106 3882.786 2865.587 2497.096 2418.470 2200.155 2381.204 1859.262 2133.680 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2994.547 1250.570 4163.459 4106.640 2100.663 3901.484 4180.323 +980000 (returns) 4040.854 1835.942 4015.743 4079.690 632.458 331.025 3967.167 3886.861 3982.367 4107.944 +985000 (returns) 2477.679 3925.750 4136.360 3193.221 3039.128 4118.549 4112.143 378.759 3390.493 3852.178 +990000 (returns) 2524.714 4057.478 2906.698 4035.252 4071.042 589.226 1500.508 3878.085 1848.528 358.842 +995000 (returns) 3897.779 4215.451 3369.686 3997.661 4202.176 2641.286 837.468 3877.305 1600.519 367.057 +1000000 (returns) 602.800 3952.641 4115.855 4030.957 4206.743 3754.789 601.050 3935.316 4131.998 901.729 diff --git a/reproductions/algorithms/mujoco/iql/reproduction_results/walker2d-medium-replay-v2_results/seed-10/evaluation_result_scalar.tsv b/reproductions/algorithms/mujoco/iql/reproduction_results/walker2d-medium-replay-v2_results/seed-10/evaluation_result_scalar.tsv new file mode 100644 index 00000000..af995cd8 --- /dev/null +++ b/reproductions/algorithms/mujoco/iql/reproduction_results/walker2d-medium-replay-v2_results/seed-10/evaluation_result_scalar.tsv @@ -0,0 +1,201 @@ +iteration mean std_dev min max median +5000 245.587 6.671 236.353 255.651 245.574 +10000 291.397 6.850 282.932 304.922 288.257 +15000 458.429 153.422 260.291 638.962 531.078 +20000 144.238 54.339 115.035 305.611 125.709 +25000 903.409 162.615 686.440 1193.747 877.347 +30000 491.731 172.162 328.041 847.900 417.992 +35000 690.403 130.517 314.875 828.083 717.668 +40000 1021.279 605.167 379.284 1934.143 860.422 +45000 925.629 131.368 766.753 1286.452 887.661 +50000 1111.711 656.415 301.629 2309.125 829.999 +55000 871.587 412.545 513.665 1385.355 560.190 +60000 1648.282 188.065 1457.275 2133.107 1619.723 +65000 2113.632 1295.572 305.238 3943.825 2564.934 +70000 1852.948 170.708 1486.672 2156.704 1834.682 +75000 2106.294 516.629 1301.838 3023.252 1959.856 +80000 3571.335 543.417 2151.076 4001.468 3850.056 +85000 2200.270 508.246 1541.310 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b/reproductions/algorithms/mujoco/iql/reproduction_results/walker2d-medium-v2_results/seed-1/evaluation_result_histogram.tsv new file mode 100644 index 00000000..3c7ef8db --- /dev/null +++ b/reproductions/algorithms/mujoco/iql/reproduction_results/walker2d-medium-v2_results/seed-1/evaluation_result_histogram.tsv @@ -0,0 +1,201 @@ +iteration(key) values +5000 (returns) 1062.116 1029.741 1069.516 1276.572 1445.257 1321.027 1152.663 1518.813 1216.059 1341.656 +10000 (returns) 1545.238 601.387 1411.030 595.734 1285.986 1647.323 1622.827 2198.164 1735.255 1426.425 +15000 (returns) 1748.162 1648.426 1809.183 1672.321 2366.603 2075.086 2790.433 1846.817 2325.046 1654.006 +20000 (returns) 335.710 1145.890 1034.144 1121.498 1015.896 1113.285 1015.790 1130.783 1139.981 1102.690 +25000 (returns) 16.151 7.223 13.009 2181.378 3437.742 2321.208 6.897 1655.823 49.326 920.639 +30000 (returns) 2761.956 2326.457 3817.201 2175.090 1671.777 3847.417 2278.646 1471.913 2131.258 3509.413 +35000 (returns) 390.339 2944.841 1633.140 1592.757 417.828 2707.357 3627.009 3799.767 1418.029 1542.417 +40000 (returns) 2764.723 3913.052 3594.910 423.049 2595.945 471.214 1973.460 1925.128 2531.414 2413.831 +45000 (returns) 1114.024 1709.058 3800.657 2465.758 4003.392 4001.000 3918.243 3781.181 3870.145 3867.198 +50000 (returns) 1648.693 2808.122 3926.299 1865.154 3942.058 2681.386 1959.221 2324.763 1585.911 2111.176 +55000 (returns) 2946.389 3564.319 3414.961 2723.478 3845.307 3742.031 3597.728 1437.494 3491.642 3153.793 +60000 (returns) 3985.183 3719.460 3753.137 3931.686 4007.889 2757.575 1617.119 3867.047 3867.901 3671.468 +65000 (returns) 3797.000 2580.581 3671.094 3940.898 2406.733 3568.401 3622.218 3197.989 3690.790 3644.027 +70000 (returns) 3820.740 3860.125 3687.994 3743.873 3829.068 3660.974 2221.090 2462.675 3788.528 1973.295 +75000 (returns) 3812.645 3818.814 3823.278 3978.505 3461.312 3766.526 2599.646 3816.823 3759.766 3208.595 +80000 (returns) 3084.170 3746.229 3507.368 2784.747 3511.927 3545.197 3530.770 3551.215 3273.585 3526.386 +85000 (returns) 3527.991 3688.386 1134.872 3633.799 3478.961 3642.634 1528.493 1171.235 3511.021 2989.476 +90000 (returns) 3860.962 3587.875 3828.763 3672.297 3438.673 3686.054 1952.372 3787.859 1729.271 3034.296 +95000 (returns) 3663.384 3744.156 3718.785 2157.326 3826.642 3694.360 3761.187 3843.700 3714.796 3746.675 +100000 (returns) 3738.168 3733.414 3643.497 3790.833 3675.967 3720.829 3653.192 3572.781 3708.865 3559.937 +105000 (returns) 3792.506 3796.919 3858.203 3795.000 3651.485 1867.177 3662.472 3683.374 3629.991 3815.467 +110000 (returns) 2208.398 2426.245 3713.733 3931.582 3787.042 3449.452 3908.026 1554.337 3734.243 3819.096 +115000 (returns) 1429.972 4034.883 1412.133 1571.869 3910.165 3833.978 3824.758 3516.011 1508.990 3742.372 +120000 (returns) 3499.645 1332.206 1315.414 3961.620 3112.553 2886.980 1362.582 1374.190 1394.612 3120.435 +125000 (returns) 3890.751 4149.640 1909.748 3842.673 1416.127 2635.183 3985.733 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HcmV?d00001 diff --git a/tests/test_functions.py b/tests/test_functions.py index 0ac16f35..cb93c30a 100644 --- a/tests/test_functions.py +++ b/tests/test_functions.py @@ -578,6 +578,16 @@ def test_compute_std_with_invalid_args(self): with pytest.raises(ValueError): RF.compute_std(variance_variable, 0.01, "dummy_add") + @pytest.mark.parametrize("expectile", [0.0, 0.25, 0.5, 0.75, 1.0]) + def test_expectile_regression(self, expectile): + x_array = np.arange(-2.0, 3.0) + x = nn.Variable.from_numpy_array(x_array) + loss = RF.expectile_regression(x, expectile) + loss.forward() + + expected = np.where(x.d > 0, expectile, 1 - expectile) * (x_array**2) + assert np.allclose(loss.d, expected) + if __name__ == "__main__": pytest.main()

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(returns) 971.310 975.917 964.506 393.987 966.876 390.878 955.265 390.764 979.604 973.924 +100000 (returns) 1128.991 1213.678 1112.313 1201.634 1062.319 1082.964 1041.401 1121.108 1073.637 1251.824 +105000 (returns) 360.510 355.933 375.233 364.116 367.172 363.575 364.020 364.124 371.724 366.757 +110000 (returns) 359.733 359.749 367.199 363.760 370.733 363.163 354.508 373.100 369.763 360.242 +115000 (returns) 358.084 357.796 354.962 363.554 362.096 356.066 352.921 356.443 360.236 363.595 +120000 (returns) 338.411 338.467 335.857 338.228 341.765 338.891 338.302 342.091 341.684 339.610 +125000 (returns) 342.504 343.179 339.191 341.602 340.956 345.963 341.603 341.663 341.551 338.727 +130000 (returns) 871.069 888.888 889.713 989.701 866.648 876.780 874.045 885.184 919.143 958.241 +135000 (returns) 59.038 39.590 43.311 55.149 43.270 43.039 39.448 45.375 37.734 336.165 +140000 (returns) 33.934 37.890 37.805 35.874 37.988 35.899 37.762 35.818 35.911 39.654 +145000 (returns) 356.007 354.330 354.816 349.544 352.948 354.659 352.124 349.396 355.961 354.017 +150000 (returns) 43.047 41.982 41.401 43.137 49.965 41.545 39.642 43.324 41.685 345.946 +155000 (returns) 662.400 639.997 752.743 655.896 762.053 2042.622 3520.377 2635.789 662.809 676.501 +160000 (returns) 355.031 358.761 343.952 60.907 343.600 355.775 340.216 336.802 348.976 355.568 +165000 (returns) 56.512 1101.144 46.746 58.701 46.359 48.797 3590.380 47.803 48.662 56.640 +170000 (returns) 59.151 57.118 54.577 53.205 52.064 53.000 51.526 47.189 55.182 50.537 +175000 (returns) 41.081 45.458 48.847 41.475 43.008 43.327 45.625 41.118 43.241 45.622 +180000 (returns) 356.085 380.069 479.427 497.485 484.771 365.153 483.685 372.710 375.141 354.241 +185000 (returns) 778.951 47.608 357.608 54.835 45.063 49.096 49.159 50.717 48.914 46.879 +190000 (returns) 353.185 342.415 350.345 36.266 339.975 347.076 38.317 345.492 37.766 353.639 +195000 (returns) 49.215 40.987 45.601 43.085 41.464 41.423 45.502 43.256 45.747 45.363 +200000 (returns) 366.732 664.871 363.486 3597.757 3604.867 365.625 636.639 3593.661 3609.038 1562.875 +205000 (returns) 362.392 360.426 362.552 370.353 45.411 363.781 364.859 365.491 361.391 47.806 +210000 (returns) 34.355 34.285 36.280 36.014 36.455 39.845 34.284 36.324 34.674 34.583 +215000 (returns) 46.770 701.459 50.644 50.658 52.720 356.941 50.059 50.411 50.239 46.560 +220000 (returns) 654.399 49.570 47.425 45.558 47.607 47.633 47.797 45.780 45.744 45.618 +225000 (returns) 356.820 53.355 367.157 47.446 49.971 47.086 370.675 374.508 370.183 49.813 +230000 (returns) 343.293 345.137 349.741 51.840 337.421 340.818 47.896 48.299 355.551 356.228 +235000 (returns) 33.087 33.421 33.220 35.129 34.887 34.967 33.577 34.975 33.289 33.332 +240000 (returns) 366.658 358.692 369.196 373.323 369.222 373.145 358.886 368.952 356.154 362.182 +245000 (returns) 50.048 52.106 3616.958 47.893 3564.969 1820.726 3014.426 3577.270 2402.334 3622.725 +250000 (returns) 47.523 49.365 46.861 47.264 47.328 44.985 45.749 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1200.356 2599.111 1525.874 +20000 1676.212 373.879 1201.389 2743.182 1800.142 +25000 1550.942 297.167 1228.817 2186.898 1511.295 +30000 1310.909 79.254 1192.311 1566.559 1286.583 +35000 1388.499 160.019 1036.702 1670.256 1346.970 +40000 1219.301 175.962 1032.425 1645.970 1158.599 +45000 1586.669 331.898 1227.771 2637.898 1548.694 +50000 1399.756 314.788 1036.043 2207.426 1317.821 +55000 1323.581 221.943 1042.000 1944.290 1272.482 +60000 1146.030 138.733 1002.232 1606.596 1071.835 +65000 1622.468 610.594 964.506 3316.734 1468.702 +70000 1311.690 123.164 1152.861 1663.879 1282.438 +75000 1672.304 403.532 1046.543 2813.886 1586.412 +80000 1287.215 361.309 968.315 2888.839 1207.591 +85000 1566.225 316.802 1240.561 2445.133 1518.974 +90000 1744.710 462.616 1219.041 3296.310 1621.526 +95000 1557.768 612.408 958.611 3337.696 1311.173 +100000 1598.221 365.979 1275.431 2571.706 1523.874 +105000 1715.394 559.948 1196.161 3244.115 1547.736 +110000 1969.209 592.184 1335.711 3371.523 1669.833 +115000 2005.448 594.715 1248.418 3341.348 1843.297 +120000 1952.850 554.676 1315.555 3360.608 1874.881 +125000 1799.051 431.402 1513.233 3281.668 1608.633 +130000 1674.745 238.361 1338.642 2420.600 1611.319 +135000 1973.772 396.956 1477.010 2821.438 1880.220 +140000 1906.439 527.035 1458.625 3271.525 1622.521 +145000 1946.368 476.747 1333.431 3070.863 1809.167 +150000 1726.878 456.551 1287.450 3345.801 1585.162 +155000 2058.226 545.504 1486.449 3281.111 1901.439 +160000 1743.246 353.423 1297.606 2623.463 1606.601 +165000 1810.302 594.046 1305.528 3356.620 1555.133 +170000 1694.614 369.761 1315.345 3316.457 1613.298 +175000 2025.562 640.281 1324.866 3265.168 1723.590 +180000 2211.533 674.886 1370.126 3353.542 1906.818 +185000 2140.348 492.659 1541.668 3331.788 2084.628 +190000 1891.291 459.161 1347.251 3281.216 1789.211 +195000 1845.785 402.426 1344.439 3051.697 1658.365 +200000 1895.257 586.335 1327.962 3222.603 1609.530 +205000 1763.569 377.062 1321.367 2742.713 1613.852 +210000 1692.172 320.295 1305.634 2430.179 1586.423 +215000 2061.777 563.274 1306.928 3339.442 1892.454 +220000 2257.042 550.904 1464.171 3284.980 2136.379 +225000 1929.902 556.541 1356.791 3282.676 1681.454 +230000 1856.043 500.231 1343.777 3276.613 1632.982 +235000 2058.521 548.790 1449.238 3266.326 1892.705 +240000 2035.992 571.850 1366.326 3372.910 1885.338 +245000 1767.569 311.247 1449.678 2845.229 1616.251 +250000 2252.766 793.703 1304.492 3359.764 2121.012 +255000 2212.470 554.760 1512.309 3306.868 2105.354 +260000 1856.557 370.090 1355.398 3092.104 1840.994 +265000 2008.162 479.863 1529.384 3104.405 1882.639 +270000 1947.818 514.157 1291.797 3309.202 1853.805 +275000 2089.933 633.062 1385.649 3309.106 1901.869 +280000 2006.127 610.657 1289.600 3308.614 1818.697 +285000 1828.037 483.938 1291.890 3304.719 1806.568 +290000 1794.389 435.969 1312.765 3297.377 1626.805 +295000 1997.448 546.949 1401.980 3270.751 1828.784 +300000 1907.725 546.071 1294.048 3276.408 1618.406 +305000 1819.759 412.974 1391.195 3246.737 1693.172 +310000 2007.835 518.375 1355.763 3351.519 1876.784 +315000 2307.313 561.505 1571.615 3344.554 1992.649 +320000 2074.894 631.540 1317.615 3246.574 1862.066 +325000 1769.498 357.430 1296.791 3233.348 1731.003 +330000 2131.707 558.690 1332.336 3334.343 1922.735 +335000 2043.860 529.144 1344.375 3263.132 1898.948 +340000 2163.176 483.180 1534.533 3366.050 1939.403 +345000 2074.494 575.233 1390.708 3321.881 1916.474 +350000 2050.694 570.257 1280.281 3237.664 1868.512 +355000 2161.030 542.306 1421.747 3277.133 1927.825 +360000 1857.665 555.586 1314.722 3308.598 1676.399 +365000 1951.215 370.536 1306.397 2860.576 1909.573 +370000 1921.881 382.034 1296.585 3098.603 1869.535 +375000 2190.104 503.498 1440.674 3300.347 2053.629 +380000 2069.794 600.006 1308.512 3303.458 1881.082 +385000 1991.642 591.841 1312.217 3363.860 1754.871 +390000 2084.668 355.905 1737.742 3291.473 1923.696 +395000 2073.492 600.958 1440.898 3290.089 1899.214 +400000 2330.299 577.260 1585.838 3359.318 2182.355 +405000 1855.653 317.963 1381.822 3225.218 1805.309 +410000 2078.109 441.252 1483.802 3280.143 1908.547 +415000 2201.762 521.771 1514.649 3348.883 1938.150 +420000 2106.305 596.234 1427.871 3303.943 1870.218 +425000 2189.380 486.762 1488.922 3343.869 1924.149 +430000 2218.550 570.054 1462.117 3314.635 1936.630 +435000 2049.568 520.261 1342.181 3350.470 1916.523 +440000 2376.090 594.149 1546.823 3317.642 2234.222 +445000 2024.509 476.286 1357.658 3124.254 1876.423 +450000 2058.715 405.521 1557.192 3321.645 1911.847 +455000 2257.511 511.664 1500.843 3226.905 2104.167 +460000 2237.752 538.193 1508.984 3293.437 1938.899 +465000 2341.254 629.524 1395.251 3357.687 2210.641 +470000 2068.921 393.824 1451.292 3326.356 1933.533 +475000 2193.086 406.523 1569.281 3323.781 2080.978 +480000 1902.390 425.448 1391.758 3268.120 1890.457 +485000 1976.167 405.562 1428.139 3285.971 1905.786 +490000 2290.491 520.787 1463.384 3335.961 2162.846 +495000 2046.511 515.955 1454.996 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