From 2116eb08e1620e6a6e136c7c62e42babd2e86c56 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marco=20Tr=C3=B6ster?= Date: Tue, 15 Aug 2023 12:31:33 +0200 Subject: [PATCH] add demo of best models --- README.md | 30 ++---- demo_defensive.py | 169 ++++++++++++++++++++++++++++++++++ demo_offensive.py | 28 ++++++ model/run_023.zip | Bin 0 -> 762233 bytes model/run_043.zip | Bin 0 -> 965180 bytes robot_sf/feature_extractor.py | 6 +- robot_sf/nav/navigation.py | 9 +- robot_sf/robot_env.py | 15 ++- robot_sf/sim/simulator.py | 10 +- 9 files changed, 230 insertions(+), 37 deletions(-) create mode 100644 demo_defensive.py create mode 100644 demo_offensive.py create mode 100755 model/run_023.zip create mode 100755 model/run_043.zip diff --git a/README.md b/README.md index b994d0c..2e25c26 100644 --- a/README.md +++ b/README.md @@ -65,7 +65,14 @@ python3 -m pytest tests python3 -m pylint robot_sf ``` -### 5. Run StableBaselines Training (Docker) +### 5. Run Visual Debugging of Pre-Trained Demo Models + +```sh +python3 demo_offensive.py +python3 demo_defensive.py +``` + +### 6. Run StableBaselines Training (Docker) ```sh docker-compose build && docker-compose run \ @@ -74,7 +81,7 @@ docker-compose build && docker-compose run \ *Note: See [this setup](./docs/GPU_SETUP.md) to install Docker with GPU support.* -### 6. Edit Maps +### 7. Edit Maps ```sh sudo apt-get update && sudo apt-get install -y python3-tk @@ -86,25 +93,6 @@ python3 -m map_editor *Note: See [this documentation](./docs/MAP_EDITOR_USAGE.md) on how to use the map editor.* -### 7. Visual Debugging - -```sh -cp scripts/debug_random_policy.py . -python3 debug_random_policy.py -``` - -```sh -mkdir model -pushd model; - wget https://megastore.rz.uni-augsburg.de/get/0oxVNm_Boo/ -O ppo_model.zip -popd -``` - -```sh -cp scripts/debug_trained_policy.py . -python3 debug_trained_policy.py -``` - ### 8. Optimize Training Hyperparams (Docker) ```sh diff --git a/demo_defensive.py b/demo_defensive.py new file mode 100644 index 0000000..9808b7a --- /dev/null +++ b/demo_defensive.py @@ -0,0 +1,169 @@ +import numpy as np +from gym import spaces +from stable_baselines3 import PPO + +from robot_sf.robot_env import RobotEnv, OBS_DRIVE_STATE, OBS_RAYS +from robot_sf.sim_config import EnvSettings +from robot_sf.sim.sim_config import SimulationSettings +from robot_sf.robot.differential_drive import DifferentialDriveSettings + + +def training(): + env_config = EnvSettings( + sim_config=SimulationSettings(stack_steps=1, difficulty=0, ped_density_by_difficulty=[0.06]), + robot_config=DifferentialDriveSettings(radius=1.0)) + env = RobotEnv(env_config, debug=True) + env.observation_space, env.action_space = prepare_gym_spaces() + model = PPO.load("./model/run_023", env=env) + + def obs_adapter(orig_obs): + drive_state = orig_obs[OBS_DRIVE_STATE] + ray_state = orig_obs[OBS_RAYS] + drive_state = drive_state[:, :-1] + drive_state[:, 2] *= 10 + drive_state = np.squeeze(drive_state) + ray_state = np.squeeze(ray_state) + return np.concatenate((ray_state, drive_state), axis=0) + + obs = env.reset() + for _ in range(10000): + obs = obs_adapter(obs) + action, _ = model.predict(obs, deterministic=True) + obs, _, done, _ = env.step(action) + env.render() + + if done: + obs = env.reset() + env.render() + env.exit() + + +def prepare_gym_spaces(): + obs_low = np.array([ 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., 0. , 0., 0., + 0., 0., 0., -0.5, 0., -3.14159265]) + + + obs_high = np.array([1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01, + 2.00000000e+00, 5.00000000e-01, 1.07480231e+03, 3.14159265e+00]) + + action_low = np.array([-2.0, -0.5]) + action_high = np.array([2.0, 0.5]) + + obs_space = spaces.Box(low=obs_low, high=obs_high, dtype=np.float64) + action_space = spaces.Box(low=action_low, high=action_high, dtype=np.float64) + return obs_space, action_space + + +if __name__ == '__main__': + training() diff --git a/demo_offensive.py b/demo_offensive.py new file mode 100644 index 0000000..4c33d9f --- /dev/null +++ b/demo_offensive.py @@ -0,0 +1,28 @@ +from stable_baselines3 import PPO +from robot_sf.robot_env import RobotEnv +from robot_sf.sim_config import EnvSettings +from robot_sf.sim.sim_config import SimulationSettings +from robot_sf.robot.bicycle_drive import BicycleDriveSettings + + +def training(): + env_config = EnvSettings( + sim_config=SimulationSettings(difficulty=0, ped_density_by_difficulty=[0.06]), + robot_config=BicycleDriveSettings(radius=0.5, max_accel=3.0, allow_backwards=True)) + env = RobotEnv(env_config, debug=True) + model = PPO.load("./model/run_043", env=env) + + obs = env.reset() + for _ in range(10000): + action, _ = model.predict(obs, deterministic=True) + obs, _, done, _ = env.step(action) + env.render() + + if done: + obs = env.reset() + env.render() + env.exit() + + +if __name__ == '__main__': + training() diff --git a/model/run_023.zip b/model/run_023.zip new file mode 100755 index 0000000000000000000000000000000000000000..59af196341f28492b0ec3b72c994747b82c4ffeb GIT binary patch literal 762233 zcmeFa*|OtE(jF)?>1O779^hEtsN3-Wg?($2#taJy5F`kY011GSEE_8c5F4=&BxLha zy48csgUkzz^PH1wXLVL}S5{?K;h1HfB^ZH#hx->E5gwnL%m4VF{;&V=`1*hU4{rb8 z|M&k_|F7MD{117$>90>Oe|;|bYj>~Xzrx@C 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literal 0 HcmV?d00001 diff --git a/robot_sf/feature_extractor.py b/robot_sf/feature_extractor.py index 26f6c28..618686c 100644 --- a/robot_sf/feature_extractor.py +++ b/robot_sf/feature_extractor.py @@ -16,9 +16,9 @@ class DynamicsExtractor(BaseFeaturesExtractor): def __init__( self, observation_space: spaces.Dict, use_ray_conv: bool=True, - num_filters: List[int]=[256, 256, 64, 32], - kernel_sizes: List[int]=[5, 5, 5, 3], - dropout_rates: List[float]=[0.1, 0.1, 0.3, 0.3]): + num_filters: List[int]=[64, 16, 16, 16], + kernel_sizes: List[int]=[3, 3, 3, 3], + dropout_rates: List[float]=[0.3, 0.3, 0.3, 0.3]): rays_space: spaces.Box = observation_space.spaces[OBS_RAYS] drive_state_space: spaces.Box = observation_space.spaces[OBS_DRIVE_STATE] drive_state_features = np.prod(drive_state_space.shape) diff --git a/robot_sf/nav/navigation.py b/robot_sf/nav/navigation.py index dd4e2e5..73410a0 100644 --- a/robot_sf/nav/navigation.py +++ b/robot_sf/nav/navigation.py @@ -1,5 +1,5 @@ from random import sample, randint -from math import dist +from math import dist, atan2 from dataclasses import dataclass, field from typing import List, Tuple, Optional @@ -32,6 +32,11 @@ def next_waypoint(self) -> Optional[Vec2D]: return self.waypoints[self.waypoint_id + 1] \ if self.waypoint_id + 1 < len(self.waypoints) else None + @property + def initial_orientation(self) -> float: + return atan2(self.waypoints[1][1] - self.waypoints[0][1], + self.waypoints[1][0] - self.waypoints[0][0]) + def update_position(self, pos: Vec2D): reached_waypoint = dist(self.current_waypoint, pos) <= self.proximity_threshold if reached_waypoint: @@ -49,7 +54,7 @@ def sample_route( spawn_id: Optional[int]=None) -> List[Vec2D]: spawn_id = spawn_id if spawn_id is not None else \ - randint(0, len(map_def.robot_spawn_zones) - 1) + randint(0, map_def.num_start_pos - 1) routes = map_def.robot_routes_by_spawn_id[spawn_id] route = sample(routes, k=1)[0] initial_spawn = sample_zone(route.spawn_zone, 1)[0] diff --git a/robot_sf/robot_env.py b/robot_sf/robot_env.py index c4c45be..046a85c 100644 --- a/robot_sf/robot_env.py +++ b/robot_sf/robot_env.py @@ -142,7 +142,9 @@ def simple_reward( return reward -def init_simulators(env_config: EnvSettings, map_def: MapDefinition, num_robots: int = 1): +def init_simulators( + env_config: EnvSettings, map_def: MapDefinition, + num_robots: int = 1, random_start_pos: bool = True) -> List[Simulator]: num_sims = ceil(num_robots / map_def.num_start_pos) goal_proximity = env_config.robot_config.radius + env_config.sim_config.goal_radius sims: List[Simulator] = [] @@ -150,7 +152,10 @@ def init_simulators(env_config: EnvSettings, map_def: MapDefinition, num_robots: for i in range(num_sims): n = map_def.num_start_pos if i < num_sims - 1 else num_robots % map_def.num_start_pos sim_robots = [env_config.robot_factory() for _ in range(n)] - sims.append(Simulator(env_config.sim_config, map_def, sim_robots, goal_proximity)) + sim = Simulator( + env_config.sim_config, map_def, sim_robots, + goal_proximity, random_start_pos) + sims.append(sim) return sims @@ -207,7 +212,7 @@ def __init__( self.action_space, self.observation_space, orig_obs_space = init_spaces(env_config, map_def) self.reward_func, self.debug = reward_func, debug - self.simulator = init_simulators(env_config, map_def)[0] + self.simulator = init_simulators(env_config, map_def, random_start_pos=True)[0] d_t = env_config.sim_config.time_per_step_in_secs max_ep_time = env_config.sim_config.sim_time_in_secs @@ -217,7 +222,7 @@ def __init__( self.last_action = None if debug: self.sim_ui = SimulationView( - scaling=6, + scaling=10, obstacles=map_def.obstacles, robot_radius=env_config.robot_config.radius, ped_radius=env_config.sim_config.ped_radius, @@ -275,7 +280,7 @@ def __init__( dtype=self.single_action_space.low.dtype) self.reward_func, self.debug = reward_func, debug - self.simulators = init_simulators(env_config, map_def, num_robots) + self.simulators = init_simulators(env_config, map_def, num_robots, random_start_pos=False) self.states: List[RobotState] = [] d_t = env_config.sim_config.time_per_step_in_secs max_ep_time = env_config.sim_config.sim_time_in_secs diff --git a/robot_sf/sim/simulator.py b/robot_sf/sim/simulator.py index b8a8e25..a2e9402 100644 --- a/robot_sf/sim/simulator.py +++ b/robot_sf/sim/simulator.py @@ -1,6 +1,5 @@ -from math import atan2 from dataclasses import dataclass, field -from typing import List, Tuple, Union, Callable +from typing import List, Tuple, Union from pysocialforce import Simulator as PySFSimulator from pysocialforce.simulator import make_forces as pysf_make_forces @@ -31,9 +30,9 @@ class Simulator: map_def: MapDefinition robots: List[Robot] goal_proximity_threshold: float + random_start_pos: bool robot_navs: List[RouteNavigator] = field(init=False) pysf_sim: PySFSimulator = field(init=False) - sample_route: Callable[[], List[Vec2D]] = field(init=False) pysf_state: PedestrianStates = field(init=False) groups: PedestrianGroupings = field(init=False) peds_behaviors: List[PedestrianBehavior] = field(init=False) @@ -90,10 +89,9 @@ def reset_state(self): collision = not nav.reached_waypoint is_at_final_goal = nav.reached_destination if collision or is_at_final_goal: - waypoints = sample_route(self.map_def, i) + waypoints = sample_route(self.map_def, None if self.random_start_pos else i) nav.new_route(waypoints[1:]) - new_orient = atan2(waypoints[1][1] - waypoints[0][1], waypoints[1][0] - waypoints[0][0]) - robot.reset_state((waypoints[0], new_orient)) + robot.reset_state((waypoints[0], nav.initial_orientation)) def step_once(self, actions: List[RobotAction]): for behavior in self.peds_behaviors:

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