From f6fee238e7dc5eb516ad7504c08fe9f45324dfbe Mon Sep 17 00:00:00 2001 From: Montgomery Alban Date: Thu, 15 Jun 2023 05:51:24 +0000 Subject: [PATCH 01/16] Update RLlib. --- examples/rl/rllib/model/saved_model.pb | Bin 436837 -> 0 bytes .../variables/variables.data-00000-of-00001 | Bin 842844 -> 0 bytes .../rl/rllib/model/variables/variables.index | Bin 891 -> 0 bytes examples/rl/rllib/rllib.py | 257 +++++++++++++----- examples/rl/rllib/rllib_agent.py | 36 ++- setup.cfg | 5 +- smarts/env/custom_observations.py | 122 +++++++-- smarts/env/rllib_hiway_env.py | 94 ++++++- smarts/env/tests/test_rllib_hiway_env.py | 11 +- smarts/env/utils/action_conversion.py | 3 +- smarts/env/utils/observation_conversion.py | 19 +- 11 files changed, 421 insertions(+), 126 deletions(-) delete mode 100644 examples/rl/rllib/model/saved_model.pb delete mode 100644 examples/rl/rllib/model/variables/variables.data-00000-of-00001 delete mode 100644 examples/rl/rllib/model/variables/variables.index diff --git 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zfI-u#A<4`D1pgQr7??neCg$!1AR5SLU^wt;;RXgq=2V8IY;ZBF(6zr;v2ko?;|B@h O2jRaPx>ZWuZvz1Echz(N diff --git a/examples/rl/rllib/rllib.py b/examples/rl/rllib/rllib.py index 63d64c4ba7..c558478ed9 100644 --- a/examples/rl/rllib/rllib.py +++ b/examples/rl/rllib/rllib.py @@ -3,7 +3,8 @@ import multiprocessing import random from pathlib import Path -from typing import Dict +from pprint import pprint as print +from typing import Dict, Literal, Union import numpy as np @@ -12,14 +13,18 @@ # whether ray[rllib] was installed by user and raises an Exception warning the user to install it if not so. try: from ray import tune - from ray.rllib.agents.callbacks import DefaultCallbacks + from ray.rllib.algorithms.callbacks import DefaultCallbacks + from ray.rllib.algorithms.pg import PGConfig from ray.rllib.env.base_env import BaseEnv - from ray.rllib.evaluation.episode import MultiAgentEpisode + from ray.rllib.evaluation.episode import Episode + from ray.rllib.evaluation.episode_v2 import EpisodeV2 from ray.rllib.evaluation.rollout_worker import RolloutWorker from ray.rllib.policy.policy import Policy from ray.rllib.utils.typing import PolicyID + from ray.tune.registry import register_env from ray.tune.schedulers import PopulationBasedTraining except Exception as e: + raise from smarts.core.utils.custom_exceptions import RayException raise RayException.required_to("rllib.py") @@ -35,7 +40,7 @@ from .rllib_agent import TrainingModel, rllib_agent logging.basicConfig(level=logging.INFO) - +register_env("rllib_hiway-v0", RLlibHiWayEnv) # Add custom metrics to your tensorboard using these callbacks # See: https://ray.readthedocs.io/en/latest/rllib-training.html#callbacks-and-custom-metrics @@ -45,43 +50,43 @@ def on_episode_start( worker: RolloutWorker, base_env: BaseEnv, policies: Dict[PolicyID, Policy], - episode: MultiAgentEpisode, + episode: Union[Episode, EpisodeV2], env_index: int, **kwargs, ): - episode.user_data["ego_speed"] = [] + episode.user_data["ego_reward"] = [] @staticmethod def on_episode_step( worker: RolloutWorker, base_env: BaseEnv, - episode: MultiAgentEpisode, + episode: Union[Episode, EpisodeV2], env_index: int, **kwargs, ): - - single_agent_id = list(episode._agent_to_last_obs)[0] - obs = episode.last_raw_obs_for(single_agent_id) - episode.user_data["ego_speed"].append(obs["speed"]) + single_agent_id = list(episode.get_agents())[0] + infos = episode._last_infos.get(single_agent_id) + if infos is not None: + episode.user_data["ego_reward"].append(infos["reward"]) @staticmethod def on_episode_end( worker: RolloutWorker, base_env: BaseEnv, policies: Dict[PolicyID, Policy], - episode: MultiAgentEpisode, + episode: Union[Episode, EpisodeV2], env_index: int, **kwargs, ): - mean_ego_speed = np.mean(episode.user_data["ego_speed"]) + mean_ego_speed = np.mean(episode.user_data["ego_reward"]) print( f"ep. {episode.episode_id:<12} ended;" f" length={episode.length:<6}" - f" mean_ego_speed={mean_ego_speed:.2f}" + f" mean_ego_reward={mean_ego_speed:.2f}" ) - episode.custom_metrics["mean_ego_speed"] = mean_ego_speed + episode.custom_metrics["mean_ego_reward"] = mean_ego_speed def explore(config): @@ -103,13 +108,78 @@ def main( num_workers, resume_training, result_dir, - checkpoint_num, + checkpoint_freq: int, + checkpoint_num: int, + log_level: Literal["DEBUG", "INFO", "WARN", "ERROR"], save_model_path, ): assert train_batch_size > 0, f"{train_batch_size.__name__} cannot be less than 1." - if rollout_fragment_length > train_batch_size: + if ( + isinstance(rollout_fragment_length, int) + and rollout_fragment_length > train_batch_size + ): rollout_fragment_length = train_batch_size + rllib_policies = { + f"AGENT-{i}": ( + None, + rllib_agent["observation_space"], + rllib_agent["action_space"], + {"model": {"custom_model": TrainingModel.NAME}}, + ) + for i in range(num_agents) + } + + smarts.core.seed(seed) + algo_config = ( + PGConfig() + .environment( + env="rllib_hiway-v0", + env_config={ + "seed": 42, + "scenarios": [str(Path(scenario).expanduser().resolve().absolute())], + "headless": not envision, + "agent_specs": { + f"AGENT-{i}": rllib_agent["agent_spec"] for i in range(num_agents) + }, + "observation_options": "multi_agent", + }, + disable_env_checking=True, + ) + .framework(framework="tf2", eager_tracing=True) + .rollouts( + rollout_fragment_length=rollout_fragment_length, + num_rollout_workers=num_workers, + num_envs_per_worker=1, + ) + .training( + lr_schedule=[(0, 1e-3), (1e3, 5e-4), (1e5, 1e-4), (1e7, 5e-5), (1e8, 1e-5)], + train_batch_size=train_batch_size, + ) + .multi_agent( + policies=rllib_policies, + policy_mapping_fn=lambda agent_id, episode, worker, **kwargs: f"{agent_id}", + ) + # .callbacks(callbacks_class=Callbacks) + .debugging(log_level=log_level) + ) + + experiment_name = "rllib_example_multi" + result_dir = Path(result_dir).expanduser().resolve().absolute() + + def get_checkpoint_dir(num): + checkpoint_dir = result_dir / f"checkpoint_{num}" / f"checkpoint-{num}" + checkpoint_dir.mkdir(parents=True, exist_ok=True) + return checkpoint_dir + + if checkpoint_num: + checkpoint = str(get_checkpoint_dir(checkpoint_num)) + else: + checkpoint = None + + print(f"======= Checkpointing at {str(result_dir)} =======") + + ## Approach 1 pbt = PopulationBasedTraining( time_attr="time_total_s", metric="episode_reward_mean", @@ -126,69 +196,95 @@ def main( # Specifies additional mutations after hyperparam_mutations is applied custom_explore_fn=explore, ) + # analysis = tune.run( + # "PG", + # name=experiment_name, + # stop={"time_total_s": time_total_s}, + # checkpoint_freq=checkpoint_freq, + # checkpoint_at_end=True, + # local_dir=str(result_dir), + # resume=resume_training, + # restore=checkpoint, + # max_failures=3, + # num_samples=num_samples, + # export_formats=["model", "checkpoint"], + # config=algo_config, + # scheduler=pbt, + # ) - # XXX: There is a bug in Ray where we can only export a trained model if - # the policy it's attached to is named 'default_policy'. - # See: https://github.com/ray-project/ray/issues/5339 - rllib_policies = { - "default_policy": ( - None, - rllib_agent["observation_space"], - rllib_agent["action_space"], - {"model": {"custom_model": TrainingModel.NAME}}, - ) - } + # print(analysis.dataframe().head()) - smarts.core.seed(seed) - tune_config = { - "env": RLlibHiWayEnv, - "log_level": "WARN", - "num_workers": num_workers, - "env_config": { - "seed": tune.sample_from(lambda spec: random.randint(0, 300)), - "scenarios": [str(Path(scenario).expanduser().resolve().absolute())], - "headless": not envision, - "agent_specs": { - f"AGENT-{i}": rllib_agent["agent_spec"] for i in range(num_agents) - }, - }, - "multiagent": {"policies": rllib_policies}, - "callbacks": Callbacks, - } + # best_logdir = Path(analysis.get_best_logdir("episode_reward_max", mode="max")) + # model_path = best_logdir / "model" - experiment_name = "rllib_example_multi" - result_dir = Path(result_dir).expanduser().resolve().absolute() - if checkpoint_num: - checkpoint = str( - result_dir / f"checkpoint_{checkpoint_num}" / f"checkpoint-{checkpoint_num}" - ) - else: - checkpoint = None + # copy_tree(str(model_path), save_model_path, overwrite=True) + # print(f"Wrote model to: {save_model_path}") - print(f"Checkpointing at {str(result_dir)}") - analysis = tune.run( - "PG", - name=experiment_name, - stop={"time_total_s": time_total_s}, - checkpoint_freq=1, - checkpoint_at_end=True, - local_dir=str(result_dir), - resume=resume_training, - restore=checkpoint, - max_failures=3, - num_samples=num_samples, - export_formats=["model", "checkpoint"], - config=tune_config, - scheduler=pbt, - ) + ## Approach 2 + from ray.rllib.algorithms.algorithm import Algorithm + + algo = algo_config.build() + if checkpoint is not None: + Algorithm.load_checkpoint(algo, checkpoint=checkpoint) + result = {} + current_iteration = 0 + checkpoint_iteration = checkpoint_num or 0 + + try: + while result.get("time_total_s", 0) < time_total_s: + result = algo.train() + print(f"======== Iteration {result['training_iteration']} ========") + print(result, depth=1) + + if current_iteration % checkpoint_freq == 0: + checkpoint_dir = get_checkpoint_dir(checkpoint_iteration) + print(f"======= Saving checkpoint {checkpoint_iteration} =======") + algo.save_checkpoint(checkpoint_dir) + checkpoint_iteration += 1 + current_iteration += 1 + algo.save_checkpoint(get_checkpoint_dir(checkpoint_iteration)) + finally: + algo.save(get_checkpoint_dir("latest")) + + algo.stop() - print(analysis.dataframe().head()) + ## Approach 3 + # from ray import air + # run_config = air.RunConfig( + # name=experiment_name, + # stop={"time_total_s": time_total_s}, + # callbacks=[Callbacks], + # storage_path=result_dir, + # checkpoint_config=air.CheckpointConfig( + # num_to_keep=3, + # checkpoint_frequency=checkpoint_freq, + # checkpoint_at_end=True, + # ), + # failure_config=air.FailureConfig( + # max_failures=3, + # fail_fast=False, + # ), + # local_dir=str(result_dir), + # ) + # tune_config = tune.TuneConfig( + # metric="episode_reward_mean", + # mode="max", + # num_samples=num_samples, + # scheduler=pbt, + # ) + # tuner = tune.Tuner( + # "PPO", + # param_space=algo_config, + # tune_config=tune_config, + # run_config=run_config, + # ) - best_logdir = Path(analysis.get_best_logdir("episode_reward_max", mode="max")) - model_path = best_logdir / "model" + # results = tuner.fit() + # # Get the best result based on a particular metric. + # best_result = results.get_best_result(metric="episode_reward_mean", mode="max") - copy_tree(str(model_path), save_model_path, overwrite=True) - print(f"Wrote model to: {save_model_path}") + # # Get the best checkpoint corresponding to the best result. + # best_checkpoint = best_result.checkpoint if __name__ == "__main__": @@ -212,8 +308,8 @@ def main( ) parser.add_argument( "--rollout_fragment_length", - type=int, - default=200, + type=str, + default="auto", help="Episodes are divided into fragments of this many steps for each rollout. In this example this will be ensured to be `1= Dict[str, np.ndarray]: +@dataclass +class _VehicleState: + speed: np.ndarray + position: np.ndarray + lane_index: int + heading: float + lane_id: str + steering: float + + +def lane_ttc(obs: Union[Observation, Dict]) -> Dict[str, np.ndarray]: """Computes time-to-collision (TTC) and distance-to-collision (DTC) using the given agent's observation. TTC and DTC are numpy arrays of shape (3,) with values for the right lane (at index [0]), current lane (at index [1]), @@ -162,18 +177,78 @@ def lane_ttc(obs: Observation) -> Dict[str, np.ndarray]: + :spelling:ignore:`ego_ttc`: Time to collision in each lane. Shape=(3,). + ego_lane_dist: Closest cars’ distance to ego in each lane. Shape=(3,). """ - ego = obs.ego_vehicle_state - waypoint_paths = obs.waypoint_paths - wps = [path[0] for path in waypoint_paths] - # distance of vehicle from center of lane + if isinstance(obs, Observation): + ego = obs.ego_vehicle_state + waypoint_paths = obs.waypoint_paths + wps = [path[0] for path in waypoint_paths] + neighborhood_vehicle_states = obs.neighborhood_vehicle_states + + # distance of vehicle from center of lane + + elif isinstance(obs, dict): + waypoint_paths = obs["waypoint_paths"] + waypoint_paths = [ + [ + Waypoint( + position[:2], + Heading(heading), + lane_id, + lane_width, + speed_limit, + lane_index, + np.nan, + ) + for position, heading, lane_id, lane_width, speed_limit, lane_index in zip( + waypoint_paths["position"][i], + waypoint_paths["heading"][i], + waypoint_paths["lane_id"][i], + waypoint_paths["lane_width"][i], + waypoint_paths["speed_limit"][i], + waypoint_paths["lane_index"][i], + ) + ] + for i in range(4) + ] + wps = [path[0] for path in waypoint_paths] + + evs = obs["ego_vehicle_state"] + ego = _VehicleState( + speed=evs["speed"], + position=evs["position"], + lane_index=evs["lane_index"], + heading=Heading(evs["heading"]), + lane_id=evs["lane_id"], + steering=evs["steering"], + ) + neighborhood_vehicle_states_dict = obs["neighborhood_vehicle_states"] + neighborhood_vehicle_states = [ + _VehicleState( + position=position, + heading=heading, + speed=speed, + lane_id=lane_id, + lane_index=lane_index, + steering=np.nan, + ) + for position, heading, speed, lane_id, lane_index in zip( + neighborhood_vehicle_states_dict["position"], + neighborhood_vehicle_states_dict["heading"], + neighborhood_vehicle_states_dict["speed"], + neighborhood_vehicle_states_dict["lane_id"], + neighborhood_vehicle_states_dict["lane_index"], + ) + ] + else: + raise NotImplementedError("Cannot generate using given observations") + closest_wp = min(wps, key=lambda wp: wp.dist_to(ego.position)) signed_dist_from_center = closest_wp.signed_lateral_error(ego.position) - lane_hwidth = closest_wp.lane_width * 0.5 - norm_dist_from_center = signed_dist_from_center / lane_hwidth - - ego_ttc, ego_lane_dist = _ego_ttc_lane_dist(obs, closest_wp.lane_index) - + lane_half_width = closest_wp.lane_width * 0.5 + norm_dist_from_center = signed_dist_from_center / lane_half_width + ego_ttc, ego_lane_dist = _ego_ttc_lane_dist( + ego, neighborhood_vehicle_states, waypoint_paths, closest_wp.lane_index + ) return { "distance_from_center": np.array([norm_dist_from_center]), "angle_error": np.array([closest_wp.relative_heading(ego.heading)]), @@ -189,16 +264,25 @@ def lane_ttc(obs: Observation) -> Dict[str, np.ndarray]: ) -def _ego_ttc_lane_dist(obs: Observation, ego_lane_index: int): - ttc_by_p, lane_dist_by_p = _ttc_by_path(obs) +def _ego_ttc_lane_dist( + ego_state: _VehicleState, + neighborhood_vehicle_states, + waypoint_paths, + ego_lane_index: int, +): + ttc_by_p, lane_dist_by_p = _ttc_by_path( + ego_state, neighborhood_vehicle_states, waypoint_paths + ) return _ego_ttc_calc(ego_lane_index, ttc_by_p, lane_dist_by_p) -def _ttc_by_path(obs: Observation): - ego = obs.ego_vehicle_state - waypoint_paths = obs.waypoint_paths - neighborhood_vehicle_states = obs.neighborhood_vehicle_states +def _ttc_by_path( + ego: _VehicleState, + neighborhood_vehicle_states, + waypoint_paths: List[List[Waypoint]], +): + neighborhood_vehicle_states = neighborhood_vehicle_states # first sum up the distance between waypoints along a path # ie. [(wp1, path1, 0), diff --git a/smarts/env/rllib_hiway_env.py b/smarts/env/rllib_hiway_env.py index 08795ee422..0a7e1e4d88 100644 --- a/smarts/env/rllib_hiway_env.py +++ b/smarts/env/rllib_hiway_env.py @@ -20,6 +20,7 @@ import logging import warnings from pathlib import Path +from typing import Dict from ray.rllib.env.multi_agent_env import MultiAgentEnv @@ -29,6 +30,12 @@ from smarts.core.local_traffic_provider import LocalTrafficProvider from smarts.core.scenario import Scenario from smarts.core.smarts import SMARTS +from smarts.env.utils.action_conversion import ActionOptions, ActionSpacesFormatter +from smarts.env.utils.observation_conversion import ( + ObservationOptions, + ObservationSpacesFormatter, +) +from smarts.zoo.agent_spec import AgentSpec class RLlibHiWayEnv(MultiAgentEnv): @@ -64,7 +71,41 @@ class RLlibHiWayEnv(MultiAgentEnv): """ def __init__(self, config): - self._log = logging.getLogger(self.__class__.__name__) + super().__init__() + + self._agent_specs: Dict[str, AgentSpec] = config["agent_specs"] + agent_interfaces = { + a_id: spec.interface for a_id, spec in self._agent_specs.items() + } + + ## ---- Required environment attributes ---- + ## See ray/rllib/env/multi_agent_env.py + self._agent_ids.update(id_ for id_ in self._agent_specs) + + action_options = ActionOptions.multi_agent + self._action_formatter = ActionSpacesFormatter( + agent_interfaces=agent_interfaces, action_options=action_options + ) + self.action_space = self._action_formatter.space + assert self.action_space is not None + + observation_options = ObservationOptions.multi_agent + self._observations_formatter = ObservationSpacesFormatter( + agent_interfaces=agent_interfaces, observation_options=observation_options + ) + self.observation_space = self._observations_formatter.space + assert self.observation_space is not None + + self._action_space_in_preferred_format = ( + self._check_if_action_space_maps_agent_id_to_sub_space() + ) + self._obs_space_in_preferred_format = ( + self._check_if_obs_space_maps_agent_id_to_sub_space() + ) + assert self._action_space_in_preferred_format is True + ## ---- /Required environment attributes ---- + + self._log = logging.getLogger(name=self.__class__.__name__) seed = int(config.get("seed", 42)) # See https://docs.ray.io/en/latest/rllib-env.html#configuring-environments @@ -77,7 +118,6 @@ def __init__(self, config): c = (a + b) * (a + b + 1) // 2 + b smarts.core.seed(seed + c) - self._agent_specs = config["agent_specs"] self._scenarios = [ str(Path(scenario).resolve()) for scenario in config["scenarios"] ] @@ -112,6 +152,7 @@ def __init__(self, config): ) self._smarts = None # Created on env.setup() self._dones_registered = 0 + self._config = config def step(self, agent_actions): """Environment step""" @@ -124,7 +165,9 @@ def step(self, agent_actions): isinstance(key, str) for key in agent_actions.keys() ), "Expected Dict[str, any]" - observations, rewards, dones, extras = self._smarts.step(agent_actions) + formatted_actions = self._action_formatter.format(agent_actions) + env_observations, rewards, dones, extras = self._smarts.step(formatted_actions) + env_observations = self._observations_formatter.format(env_observations) # Agent termination: RLlib expects that we return a "last observation" # on the step that an agent transitions to "done". All subsequent calls @@ -135,18 +178,18 @@ def step(self, agent_actions): # to only agents who are actively sending in actions. observations = { agent_id: obs - for agent_id, obs in observations.items() - if agent_id in agent_actions + for agent_id, obs in env_observations.items() + if agent_id in formatted_actions } rewards = { agent_id: reward for agent_id, reward in rewards.items() - if agent_id in agent_actions + if agent_id in formatted_actions } scores = { agent_id: score for agent_id, score in extras["scores"].items() - if agent_id in agent_actions + if agent_id in formatted_actions } infos = {key: {"score": value} for key, value in scores.items()} @@ -160,7 +203,7 @@ def step(self, agent_actions): ) for agent_id in agent_actions: agent_spec = self._agent_specs[agent_id] - observation = observations[agent_id] + observation = env_observations[agent_id] reward = rewards[agent_id] info = infos[agent_id] @@ -172,25 +215,50 @@ def step(self, agent_actions): self._dones_registered += 1 if done else 0 dones["__all__"] = self._dones_registered >= len(self._agent_specs) - return observations, rewards, dones, infos + return ( + observations, + rewards, + dones, + dones, + infos, + ) - def reset(self): + def reset(self, *, seed=None, options=None): """Environment reset.""" + if seed is not None: + a = self._config.worker_index + b = self._config.vector_index + c = (a + b) * (a + b + 1) // 2 + b + smarts.core.seed(seed + c) + scenario = next(self._scenarios_iterator) self._dones_registered = 0 if self._smarts is None: self._smarts = self._build_smarts() - self._smarts.setup(scenario) + self._smarts.setup(scenario=scenario) - env_observations = self._smarts.reset(scenario) + env_observations = self._smarts.reset(scenario=scenario) + env_observations = self._observations_formatter.format( + observations=env_observations + ) observations = { agent_id: self._agent_specs[agent_id].observation_adapter(obs) for agent_id, obs in env_observations.items() } + info = { + agent_id: { + "score": 0, + "env_obs": agent_obs, + "done": False, + "reward": 0, + "map_source": self._smarts.scenario.road_map.source, + } + for agent_id, agent_obs in observations.items() + } - return observations + return observations, info def close(self): """Environment close.""" diff --git a/smarts/env/tests/test_rllib_hiway_env.py b/smarts/env/tests/test_rllib_hiway_env.py index 12373c6761..13496ca3f4 100644 --- a/smarts/env/tests/test_rllib_hiway_env.py +++ b/smarts/env/tests/test_rllib_hiway_env.py @@ -67,12 +67,15 @@ class atdict(dict): __delattr__ = dict.__delitem__ env = RLlibHiWayEnv(config=atdict(**env_config, worker_index=0, vector_index=1)) - agent_ids = list(env_config["agent_specs"].keys()) + agent_ids = env.get_agent_ids() + assert isinstance(agent_ids, set) + assert AGENT_ID in agent_ids + print(env.observation_space) - dones = {"__all__": False} + term = {"__all__": False} env.reset() - while not dones["__all__"]: - _, _, dones, _ = env.step( + while not term["__all__"]: + _, _, term, _, _ = env.step( {aid: rllib_agent["action_space"].sample() for aid in agent_ids} ) env.close() diff --git a/smarts/env/utils/action_conversion.py b/smarts/env/utils/action_conversion.py index 0cbafe6fcd..7e286f3a03 100644 --- a/smarts/env/utils/action_conversion.py +++ b/smarts/env/utils/action_conversion.py @@ -246,9 +246,10 @@ def format(self, actions: Dict[str, Any]): format_ = formatting_groups[agent_interface.action] space: gym.Space = self.space[agent_id] assert space is format_.space + dtype = action.dtype if isinstance(action, np.ndarray) else None assert space.contains( action - ), f"Action {action} does not match space {space}!" + ), f"Action {action} of type `{type(action)}` & {dtype} does not match space {space}!" formatted_action = format_.formatting_func(action) out_actions[agent_id] = formatted_action diff --git a/smarts/env/utils/observation_conversion.py b/smarts/env/utils/observation_conversion.py index af7ee7bb6a..22301a1f4c 100644 --- a/smarts/env/utils/observation_conversion.py +++ b/smarts/env/utils/observation_conversion.py @@ -74,7 +74,8 @@ def _format_id(lane_id: str, max_length, type_): warnings.warn( f"`{type_}` named `{lane_name}` is more than " f"`{max_length}` characters long. It will be truncated " - "and may cause unintended issues with navigation and lane identification." + "and may cause unintended issues with navigation and lane identification.", + category=UserWarning, ) return lane_name[:max_length] @@ -193,10 +194,11 @@ def _format_neighborhood_vehicle_states( "box": np.zeros((des_shp, 3), dtype=np.float32), "heading": np.zeros((des_shp,), dtype=np.float32), "id": [""] * des_shp, + "interest": np.zeros((des_shp,), dtype=np.int8), + "lane_id": [""] * des_shp, "lane_index": np.zeros((des_shp,), dtype=np.int8), "position": np.zeros((des_shp, 3), dtype=np.float64), "speed": np.zeros((des_shp,), dtype=np.float32), - "interest": np.zeros((des_shp,), dtype=np.int8), } neighborhood_vehicle_states = [ @@ -207,11 +209,12 @@ def _format_neighborhood_vehicle_states( nghb.lane_index, nghb.position, nghb.speed, + _format_id(nghb.lane_id, _WAYPOINT_NAME_LIMIT, "lane id"), nghb.interest, ) for nghb in neighborhood_vehicle_states[:des_shp] ] - box, heading, vehicle_id, lane_index, pos, speed, interest = zip( + box, heading, vehicle_id, lane_index, pos, speed, lane_id, interest = zip( *neighborhood_vehicle_states ) @@ -229,6 +232,7 @@ def _format_neighborhood_vehicle_states( lane_index = np.pad(lane_index, ((0,pad_shp)), mode='constant', constant_values=0) pos = np.pad(pos, ((0,pad_shp),(0,0)), mode='constant', constant_values=0) speed = np.pad(speed, ((0,pad_shp)), mode='constant', constant_values=0) + lane_id = tuple(lane_id + ("",) * pad_shp) interest = np.pad(interest, ((0,pad_shp)), mode="constant", constant_values=False) # fmt: on @@ -236,10 +240,11 @@ def _format_neighborhood_vehicle_states( "box": box, "heading": heading, "id": vehicle_id, + "interest": interest, + "lane_id": lane_id, "lane_index": lane_index, "position": pos, "speed": speed, - "interest": interest, } @@ -1022,6 +1027,12 @@ class ObservationSpacesFormatter: "id": The vehicle ids of neighbor vehicles. Defaults to str("") per vehicle. shape=(10,Text(50)) + "interest": + If the vehicles are of interest. Defaults to np.array([False]) per vehicle. + shape=(10,). dtype=np.int8 + "lane_id": + The ID of the lane that the vehicle is on. Defaults to str("") per vehicle. + shape=(10,Text(50)) "lane_index": Lane number of neighbor vehicles. Defaults to np.array([0]) per vehicle. shape=(10,). dtype=np.int8. From 531bc308642ff091dce6b161d8d05ccd3276106d Mon Sep 17 00:00:00 2001 From: Montgomery Alban Date: Thu, 15 Jun 2023 05:56:45 +0000 Subject: [PATCH 02/16] Update changelog. --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 44be7ff454..b4de26f7c3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -13,6 +13,7 @@ Copy and pasting the git commit messages is __NOT__ enough. ### Changed ### Deprecated ### Fixed +- Missing neighborhood vehicle state `'lane_id'` is now added to the `hiway-v1` formatted observations. ### Removed ### Security From c1336f3d40f17bec08c709d69e3039642141e2c6 Mon Sep 17 00:00:00 2001 From: Montgomery Alban Date: Thu, 15 Jun 2023 05:58:22 +0000 Subject: [PATCH 03/16] Fix gymnasium version. --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index ad9b00b6b3..93e463ba8e 100644 --- a/setup.cfg +++ b/setup.cfg @@ -36,7 +36,7 @@ install_requires = twisted>=21.7.0 # for scenario requirements.txt files # The following are planned to be made optional - gymnasium==0.27.0 + gymnasium>=0.26.3 pybullet>=3,<4.0 # The following are planned for removal From 7acdea251a035988d7b5291b274974bc79102918 Mon Sep 17 00:00:00 2001 From: Montgomery Alban Date: Thu, 15 Jun 2023 14:14:09 +0000 Subject: [PATCH 04/16] Fix rllib example test. --- examples/tests/test_examples.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/examples/tests/test_examples.py b/examples/tests/test_examples.py index f92d0b54e1..8ff7470e2e 100644 --- a/examples/tests/test_examples.py +++ b/examples/tests/test_examples.py @@ -57,5 +57,7 @@ def test_rllib_example(): resume_training=False, result_dir=result_dir, checkpoint_num=None, + checkpoint_freq=1, save_model_path=model_dir, + log_level="WARN", ) From 9e452fd2e0f57bded77e433533a7d5c14e898c87 Mon Sep 17 00:00:00 2001 From: Montgomery Alban Date: Thu, 15 Jun 2023 15:30:43 +0000 Subject: [PATCH 05/16] Fix typing test. --- examples/rl/rllib/rllib.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/rl/rllib/rllib.py b/examples/rl/rllib/rllib.py index c558478ed9..9260599c83 100644 --- a/examples/rl/rllib/rllib.py +++ b/examples/rl/rllib/rllib.py @@ -4,7 +4,7 @@ import random from pathlib import Path from pprint import pprint as print -from typing import Dict, Literal, Union +from typing import Dict, Literal, Optional, Union import numpy as np @@ -109,7 +109,7 @@ def main( resume_training, result_dir, checkpoint_freq: int, - checkpoint_num: int, + checkpoint_num: Optional[int], log_level: Literal["DEBUG", "INFO", "WARN", "ERROR"], save_model_path, ): From f729cd535d582ed07f023518129b0d704c600b9f Mon Sep 17 00:00:00 2001 From: Montgomery Alban Date: Mon, 19 Jun 2023 16:25:43 +0000 Subject: [PATCH 06/16] Update examples. --- docs/ecosystem/rllib.rst | 4 +- examples/rl/rllib/configs.py | 149 +++++++++++++ examples/rl/rllib/pg_example.py | 190 +++++++++++++++++ examples/rl/rllib/rllib.py | 333 ++++++++++-------------------- examples/rl/rllib/rllib_agent.py | 6 +- examples/tests/test_examples.py | 26 ++- smarts/env/custom_observations.py | 6 +- smarts/env/rllib_hiway_env.py | 20 +- 8 files changed, 486 insertions(+), 248 deletions(-) create mode 100644 examples/rl/rllib/configs.py create mode 100644 examples/rl/rllib/pg_example.py diff --git a/docs/ecosystem/rllib.rst b/docs/ecosystem/rllib.rst index 2a49341ae8..8e4b031f1c 100644 --- a/docs/ecosystem/rllib.rst +++ b/docs/ecosystem/rllib.rst @@ -28,7 +28,7 @@ many docs about ``Ray`` and ``RLlib``. We recommend to read the following pages Resume training --------------- -With respect to ``SMARTS/examples/rl/rllib`` example, if you want to continue an aborted experiment, you can set ``resume=True`` in ``tune.run``. But note that ``resume=True`` will continue to use the same configuration as was set in the original experiment. +With respect to ``SMARTS/examples/rl/rllib`` examples, if you want to continue an aborted experiment, you can set ``resume_training=True``. But note that ``resume_training=True`` will continue to use the same configuration as was set in the original experiment. To make changes to a started experiment, you can edit the latest experiment file in ``./results``. -Or if you want to start a new experiment but train from an existing checkpoint, you can set ``restore=checkpoint_path`` in ``tune.run``. +Or if you want to start a new experiment but train from an existing checkpoint, you will need to look into `How to Save and Load Trial Checkpoints `_. diff --git a/examples/rl/rllib/configs.py b/examples/rl/rllib/configs.py new file mode 100644 index 0000000000..841b8f1cdc --- /dev/null +++ b/examples/rl/rllib/configs.py @@ -0,0 +1,149 @@ +import argparse +import multiprocessing +from pathlib import Path +from typing import Any, Dict, Literal, Optional, Union + +try: + from ray.rllib.algorithms.algorithm import AlgorithmConfig + from ray.rllib.algorithms.callbacks import DefaultCallbacks + from ray.rllib.algorithms.pg import PGConfig + from ray.tune.search.sample import Integer as IntegerDomain +except Exception as e: + from smarts.core.utils.custom_exceptions import RayException + + raise RayException.required_to("rllib.py") + + +def gen_pg_config( + scenario, + envision, + rollout_fragment_length, + train_batch_size, + num_workers, + log_level: Literal["DEBUG", "INFO", "WARN", "ERROR"], + seed: Union[int, IntegerDomain], + rllib_policies: Dict[str, Any], + agent_specs: Dict[str, Any], + callbacks: Optional[DefaultCallbacks], +) -> AlgorithmConfig: + assert len(set(rllib_policies.keys()).difference(agent_specs)) == 0 + algo_config = ( + PGConfig() + .environment( + env="rllib_hiway-v0", + env_config={ + "seed": seed, + "scenarios": [str(Path(scenario).expanduser().resolve().absolute())], + "headless": not envision, + "agent_specs": agent_specs, + "observation_options": "multi_agent", + }, + disable_env_checking=True, + ) + .framework(framework="tf2", eager_tracing=True) + .rollouts( + rollout_fragment_length=rollout_fragment_length, + num_rollout_workers=num_workers, + num_envs_per_worker=1, + enable_tf1_exec_eagerly=True, + ) + .training( + lr_schedule=[(0, 1e-3), (1e3, 5e-4), (1e5, 1e-4), (1e7, 5e-5), (1e8, 1e-5)], + train_batch_size=train_batch_size, + ) + .multi_agent( + policies=rllib_policies, + policy_mapping_fn=lambda agent_id, episode, worker, **kwargs: f"{agent_id}", + ) + .callbacks(callbacks_class=callbacks) + .debugging(log_level=log_level) + ) + return algo_config + + +def gen_parser( + prog: str, default_result_dir: str, default_save_model_path: str +) -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(prog) + parser.add_argument( + "--scenario", + type=str, + default=str(Path(__file__).resolve().parents[3] / "scenarios/sumo/loop"), + help="Scenario to run (see scenarios/ for some samples you can use)", + ) + parser.add_argument( + "--envision", + action="store_true", + help="Run simulation with Envision display.", + ) + parser.add_argument( + "--num_samples", + type=int, + default=1, + help="Number of times to sample from hyperparameter space", + ) + parser.add_argument( + "--rollout_fragment_length", + type=str, + default="auto", + help="Episodes are divided into fragments of this many steps for each rollout. In this example this will be ensured to be `1= Date: Tue, 20 Jun 2023 14:09:55 +0000 Subject: [PATCH 07/16] Final updates. --- CHANGELOG.md | 6 +++++ README.md | 2 ++ docs/ecosystem/rllib.rst | 7 ++++++ docs/sim/env.rst | 9 +++++--- examples/rl/rllib/configs.py | 20 +---------------- examples/rl/rllib/model/README.md | 2 +- examples/rl/rllib/pg_example.py | 16 ++++++-------- .../rl/rllib/{rllib.py => pg_pbt_example.py} | 22 +++++++++++++------ examples/tests/test_examples.py | 7 ++---- 9 files changed, 47 insertions(+), 44 deletions(-) rename examples/rl/rllib/{rllib.py => pg_pbt_example.py} (93%) diff --git a/CHANGELOG.md b/CHANGELOG.md index b4de26f7c3..d5d39833eb 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -10,7 +10,13 @@ Copy and pasting the git commit messages is __NOT__ enough. ## [Unreleased] ### Added +- Added `rllib/pg_example.py` to demonstrate a simple integration with `RLlib` and `tensorflow` for policy training. +- Added `rllib/pg_pbt_example.py` to demonstrate integration with `ray.RLlib`, `tensorflow`, and `ray.tune` for scheduled policy training. ### Changed +- Updated `smarts[ray]` (`ray==2.2`) and `smarts[rllib]` (`ray[rllib]==1.4`) to use `ray~=2.5`. +- Introduced `tensorflow-probability` to `smarts[rllib]`. +- Updated `RLlibHiWayEnv` to use the `gymnasium` interface. +- Renamed `rllib/rllib.py` to `rllib/pg_pbt_example.py`. ### Deprecated ### Fixed - Missing neighborhood vehicle state `'lane_id'` is now added to the `hiway-v1` formatted observations. diff --git a/README.md b/README.md index 3857ca5090..6067a0eabb 100644 --- a/README.md +++ b/README.md @@ -44,6 +44,8 @@ Several agent control policies and agent [action types](smarts/core/controllers/ ### RL Model 1. [Drive](examples/rl/drive). See [Driving SMARTS 2023.1 & 2023.2](https://smarts.readthedocs.io/en/latest/benchmarks/driving_smarts_2023_1.html) for more info. 1. [VehicleFollowing](examples/rl/platoon). See [Driving SMARTS 2023.3](https://smarts.readthedocs.io/en/latest/benchmarks/driving_smarts_2023_3.html) for more info. +1. [PG](examples/rl/rllib/pg_example.py). See [RLlib](https://smarts.readthedocs.io/en/latest/docs/ecosystem/rllib.html) for more info. +1. [PG Population Based Training](examples/rl/rllib/pg_pbt_example.py). See [RLlib](https://smarts.readthedocs.io/en/latest/docs/ecosystem/rllib.html) for more info. ### RL Environment 1. [ULTRA](https://github.com/smarts-project/smarts-project.rl/blob/master/ultra) provides a gym-based environment built upon SMARTS to tackle intersection navigation, specifically the unprotected left turn. diff --git a/docs/ecosystem/rllib.rst b/docs/ecosystem/rllib.rst index 8e4b031f1c..7ae554cb99 100644 --- a/docs/ecosystem/rllib.rst +++ b/docs/ecosystem/rllib.rst @@ -8,6 +8,13 @@ RLlib of applications. ``RLlib`` natively supports ``TensorFlow``, ``TensorFlow Eager``, and ``PyTorch``. Most of its internals are agnostic to such deep learning frameworks. +SMARTS contains two examples using `Policy Gradients (PG) `_. + +1. rllib/pg_example.py +This example shows the basics of using RLlib with SMARTS through :class:`~smarts.env.rllib_hiway_env.RLlibHiWayEnv`. +1. rllib/pg_pbt_example.py +This example combines Policy Gradients with `Population Based Training (PBT) `_ scheduling. + Recommended reads ----------------- diff --git a/docs/sim/env.rst b/docs/sim/env.rst index 5d6571830a..a3e4ac9cd1 100644 --- a/docs/sim/env.rst +++ b/docs/sim/env.rst @@ -9,7 +9,7 @@ Base environments SMARTS environment module is defined in :mod:`~smarts.env` package. Currently SMARTS provides two kinds of training environments, namely: -+ ``HiWayEnv`` utilizing ``gym.env`` style interface ++ ``HiWayEnv`` utilizing a ``gymnasium.Env`` interface + ``RLlibHiwayEnv`` customized for `RLlib `_ training .. image:: ../_static/env.png @@ -17,11 +17,12 @@ environments, namely: HiWayEnv ^^^^^^^^ -``HiWayEnv`` inherits class ``gym.Env`` and supports gym APIs like ``reset``, ``step``, ``close``. An usage example is shown below. +``HiWayEnv`` inherits class ``gymnasium.Env`` and supports gym APIs like ``reset``, ``step``, ``close``. An usage example is shown below. Refer to :class:`~smarts.env.hiway_env.HiWayEnv` for more details. .. code-block:: python + import gymnasium as gym # Make env env = gym.make( "smarts.env:hiway-v0", # Env entry name. @@ -53,6 +54,7 @@ exactly matches the `env.observation_space`, and `ObservationOptions.multi_agent .. code-block:: python + import gymnasium as gym # Make env env = gym.make( "smarts.env:hiway-v1", # Env entry name. @@ -81,6 +83,7 @@ This can be done with :class:`~smarts.env.gymnasium.wrappers.api_reversion.Api02 .. code-block:: python + import gymnasium as gym # Make env env = gym.make( "smarts.env:hiway-v1", # Env entry name. @@ -91,7 +94,7 @@ This can be done with :class:`~smarts.env.gymnasium.wrappers.api_reversion.Api02 RLlibHiwayEnv ^^^^^^^^^^^^^ -``RLlibHiwayEnv`` inherits class ``MultiAgentEnv``, which is defined in `RLlib `_. It also supports common env APIs like ``reset``, +``RLlibHiwayEnv`` inherits class ``MultiAgentEnv``, which is defined in `RLlib `_. It also supports common environment APIs like ``reset``, ``step``, ``close``. An usage example is shown below. Refer to :class:`~smarts.env.rllib_hiway_env.RLlibHiWayEnv` for more details. .. code-block:: python diff --git a/examples/rl/rllib/configs.py b/examples/rl/rllib/configs.py index 841b8f1cdc..266e39d841 100644 --- a/examples/rl/rllib/configs.py +++ b/examples/rl/rllib/configs.py @@ -61,9 +61,7 @@ def gen_pg_config( return algo_config -def gen_parser( - prog: str, default_result_dir: str, default_save_model_path: str -) -> argparse.ArgumentParser: +def gen_parser(prog: str, default_result_dir: str) -> argparse.ArgumentParser: parser = argparse.ArgumentParser(prog) parser.add_argument( "--scenario", @@ -76,12 +74,6 @@ def gen_parser( action="store_true", help="Run simulation with Envision display.", ) - parser.add_argument( - "--num_samples", - type=int, - default=1, - help="Number of times to sample from hyperparameter space", - ) parser.add_argument( "--rollout_fragment_length", type=str, @@ -133,17 +125,7 @@ def gen_parser( default="ERROR", help="Log level (DEBUG|INFO|WARN|ERROR)", ) - parser.add_argument( - "--checkpoint_num", type=int, default=None, help="Checkpoint number" - ) parser.add_argument( "--checkpoint_freq", type=int, default=3, help="Checkpoint frequency" ) - - parser.add_argument( - "--save_model_path", - type=str, - default=default_save_model_path, - help="Destination path of where to copy the model when training is over", - ) return parser diff --git a/examples/rl/rllib/model/README.md b/examples/rl/rllib/model/README.md index 646c9015c8..9afe8800ce 100644 --- a/examples/rl/rllib/model/README.md +++ b/examples/rl/rllib/model/README.md @@ -1,3 +1,3 @@ ## Model Binaries -The binaries located in this directory are the components of a trained rllib model. These are related to the `examples/rl/rllib/rllib.py` example script. Results from `examples/rl/rllib/rllib.py` are loaded and written to this directory. \ No newline at end of file +The binaries located in this directory are the components of a trained rllib model. These are related to the `examples/rl/rllib/pg_pbt_example.py` example script. Results from `examples/rl/rllib/pg_pbt_example.py` are loaded and written to this directory. \ No newline at end of file diff --git a/examples/rl/rllib/pg_example.py b/examples/rl/rllib/pg_example.py index f422dfa435..ec340bba05 100644 --- a/examples/rl/rllib/pg_example.py +++ b/examples/rl/rllib/pg_example.py @@ -81,7 +81,6 @@ def main( rollout_fragment_length, train_batch_size, seed, - num_samples, num_agents, num_workers, resume_training, @@ -89,7 +88,6 @@ def main( checkpoint_freq: int, checkpoint_num: Optional[int], log_level: Literal["DEBUG", "INFO", "WARN", "ERROR"], - save_model_path, ): agent_values = { "agent_specs": { @@ -162,12 +160,14 @@ def get_checkpoint_dir(num): if __name__ == "__main__": - default_save_model_path = str( - Path(__file__).expanduser().resolve().parent / "pg_model" - ) default_result_dir = str(Path(__file__).resolve().parent / "results" / "pg_results") - parser = gen_parser("rllib-example", default_result_dir, default_save_model_path) - + parser = gen_parser("rllib-example", default_result_dir) + parser.add_argument( + "--checkpoint_num", + type=int, + default=None, + help="The checkpoint number to restart from.", + ) args = parser.parse_args() build_scenario(scenario=args.scenario, clean=False, seed=42) @@ -178,7 +178,6 @@ def get_checkpoint_dir(num): rollout_fragment_length=args.rollout_fragment_length, train_batch_size=args.train_batch_size, seed=args.seed, - num_samples=args.num_samples, num_agents=args.num_agents, num_workers=args.num_workers, resume_training=args.resume_training, @@ -186,5 +185,4 @@ def get_checkpoint_dir(num): checkpoint_freq=max(args.checkpoint_freq, 1), checkpoint_num=args.checkpoint_num, log_level=args.log_level, - save_model_path=args.save_model_path, ) diff --git a/examples/rl/rllib/rllib.py b/examples/rl/rllib/pg_pbt_example.py similarity index 93% rename from examples/rl/rllib/rllib.py rename to examples/rl/rllib/pg_pbt_example.py index 6c3672251f..4fa50c53d0 100644 --- a/examples/rl/rllib/rllib.py +++ b/examples/rl/rllib/pg_pbt_example.py @@ -124,7 +124,6 @@ def main( resume_training, result_dir, checkpoint_freq: int, - checkpoint_num: Optional[int], log_level: Literal["DEBUG", "INFO", "WARN", "ERROR"], save_model_path, ): @@ -240,12 +239,22 @@ def main( if __name__ == "__main__": - default_save_model_path = str( - Path(__file__).expanduser().resolve().parent / "model" + default_result_dir = str( + Path(__file__).resolve().parent / "results" / "tune_pg_results" + ) + parser = gen_parser("rllib-example", default_result_dir) + parser.add_argument( + "--num_samples", + type=int, + default=1, + help="Number of times to sample from hyperparameter space", + ) + parser.add_argument( + "--save_model_path", + type=str, + default=str(Path(__file__).expanduser().resolve().parent / "model"), + help="Destination path of where to copy the model when training is over", ) - default_result_dir = str(Path(__file__).resolve().parent / "results" / "tune_pg_results") - parser = gen_parser("rllib-example", default_result_dir, default_save_model_path) - args = parser.parse_args() build_scenario(scenario=args.scenario, clean=False, seed=42) @@ -262,7 +271,6 @@ def main( resume_training=args.resume_training, result_dir=args.result_dir, checkpoint_freq=max(args.checkpoint_freq, 1), - checkpoint_num=args.checkpoint_num, log_level=args.log_level, save_model_path=args.save_model_path, ) diff --git a/examples/tests/test_examples.py b/examples/tests/test_examples.py index f148331d2c..3e836368f6 100644 --- a/examples/tests/test_examples.py +++ b/examples/tests/test_examples.py @@ -51,22 +51,20 @@ def test_rllib_pg_example(): rollout_fragment_length=200, train_batch_size=200, seed=42, - num_samples=1, num_agents=2, num_workers=1, resume_training=False, result_dir=result_dir, checkpoint_num=None, checkpoint_freq=1, - save_model_path=model_dir, log_level="WARN", ) def test_rllib_tune_pg_example(): - from examples.rl.rllib import rllib + from examples.rl.rllib import pg_pbt_example - main = rllib.main + main = pg_pbt_example.main with tempfile.TemporaryDirectory() as result_dir, tempfile.TemporaryDirectory() as model_dir: main( scenario="scenarios/sumo/loop", @@ -80,7 +78,6 @@ def test_rllib_tune_pg_example(): num_workers=1, resume_training=False, result_dir=result_dir, - checkpoint_num=None, checkpoint_freq=1, save_model_path=model_dir, log_level="WARN", From db03de5e822e688125b07094464439dd613176bc Mon Sep 17 00:00:00 2001 From: Montgomery Alban Date: Tue, 20 Jun 2023 15:16:09 +0000 Subject: [PATCH 08/16] Fix tests. --- docs/ecosystem/rllib.rst | 4 +- examples/rl/rllib/configs.py | 58 ----------------------------- examples/rl/rllib/pg_example.py | 51 ++++++++++++++++++------- examples/rl/rllib/pg_pbt_example.py | 50 ++++++++++++++++++------- 4 files changed, 75 insertions(+), 88 deletions(-) diff --git a/docs/ecosystem/rllib.rst b/docs/ecosystem/rllib.rst index 7ae554cb99..0b09585d4a 100644 --- a/docs/ecosystem/rllib.rst +++ b/docs/ecosystem/rllib.rst @@ -10,9 +10,9 @@ deep learning frameworks. SMARTS contains two examples using `Policy Gradients (PG) `_. -1. rllib/pg_example.py +1. ``rllib/pg_example.py`` This example shows the basics of using RLlib with SMARTS through :class:`~smarts.env.rllib_hiway_env.RLlibHiWayEnv`. -1. rllib/pg_pbt_example.py +1. ``rllib/pg_pbt_example.py`` This example combines Policy Gradients with `Population Based Training (PBT) `_ scheduling. Recommended reads diff --git a/examples/rl/rllib/configs.py b/examples/rl/rllib/configs.py index 266e39d841..48ab2853de 100644 --- a/examples/rl/rllib/configs.py +++ b/examples/rl/rllib/configs.py @@ -1,64 +1,6 @@ import argparse import multiprocessing from pathlib import Path -from typing import Any, Dict, Literal, Optional, Union - -try: - from ray.rllib.algorithms.algorithm import AlgorithmConfig - from ray.rllib.algorithms.callbacks import DefaultCallbacks - from ray.rllib.algorithms.pg import PGConfig - from ray.tune.search.sample import Integer as IntegerDomain -except Exception as e: - from smarts.core.utils.custom_exceptions import RayException - - raise RayException.required_to("rllib.py") - - -def gen_pg_config( - scenario, - envision, - rollout_fragment_length, - train_batch_size, - num_workers, - log_level: Literal["DEBUG", "INFO", "WARN", "ERROR"], - seed: Union[int, IntegerDomain], - rllib_policies: Dict[str, Any], - agent_specs: Dict[str, Any], - callbacks: Optional[DefaultCallbacks], -) -> AlgorithmConfig: - assert len(set(rllib_policies.keys()).difference(agent_specs)) == 0 - algo_config = ( - PGConfig() - .environment( - env="rllib_hiway-v0", - env_config={ - "seed": seed, - "scenarios": [str(Path(scenario).expanduser().resolve().absolute())], - "headless": not envision, - "agent_specs": agent_specs, - "observation_options": "multi_agent", - }, - disable_env_checking=True, - ) - .framework(framework="tf2", eager_tracing=True) - .rollouts( - rollout_fragment_length=rollout_fragment_length, - num_rollout_workers=num_workers, - num_envs_per_worker=1, - enable_tf1_exec_eagerly=True, - ) - .training( - lr_schedule=[(0, 1e-3), (1e3, 5e-4), (1e5, 1e-4), (1e7, 5e-5), (1e8, 1e-5)], - train_batch_size=train_batch_size, - ) - .multi_agent( - policies=rllib_policies, - policy_mapping_fn=lambda agent_id, episode, worker, **kwargs: f"{agent_id}", - ) - .callbacks(callbacks_class=callbacks) - .debugging(log_level=log_level) - ) - return algo_config def gen_parser(prog: str, default_result_dir: str) -> argparse.ArgumentParser: diff --git a/examples/rl/rllib/pg_example.py b/examples/rl/rllib/pg_example.py index ec340bba05..3d82678028 100644 --- a/examples/rl/rllib/pg_example.py +++ b/examples/rl/rllib/pg_example.py @@ -1,4 +1,5 @@ from pathlib import Path +from pprint import pprint as print from typing import Dict, Literal, Optional, Union import numpy as np @@ -6,6 +7,7 @@ try: from ray.rllib.algorithms.algorithm import Algorithm, AlgorithmConfig from ray.rllib.algorithms.callbacks import DefaultCallbacks + from ray.rllib.algorithms.pg import PGConfig from ray.rllib.env.base_env import BaseEnv from ray.rllib.evaluation.episode import Episode from ray.rllib.evaluation.episode_v2 import EpisodeV2 @@ -18,13 +20,14 @@ raise RayException.required_to("rllib.py") import smarts +from smarts.env.rllib_hiway_env import RLlibHiWayEnv from smarts.sstudio.scenario_construction import build_scenario if __name__ == "__main__": - from configs import gen_parser, gen_pg_config + from configs import gen_parser from rllib_agent import TrainingModel, rllib_agent else: - from .configs import gen_parser, gen_pg_config + from .configs import gen_parser from .rllib_agent import TrainingModel, rllib_agent # Add custom metrics to your tensorboard using these callbacks @@ -107,21 +110,41 @@ def main( agent_specs = agent_values["agent_specs"] smarts.core.seed(seed) - algo_config: AlgorithmConfig = gen_pg_config( - scenario=scenario, - envision=envision, - rollout_fragment_length=rollout_fragment_length, - train_batch_size=train_batch_size, - num_workers=num_workers, - seed=seed, - log_level=log_level, - rllib_policies=rllib_policies, - agent_specs=agent_specs, - callbacks=Callbacks, + assert len(set(rllib_policies.keys()).difference(agent_specs)) == 0 + algo_config: AlgorithmConfig = ( + PGConfig() + .environment( + env=RLlibHiWayEnv, + env_config={ + "seed": seed, + "scenarios": [str(Path(scenario).expanduser().resolve().absolute())], + "headless": not envision, + "agent_specs": agent_specs, + "observation_options": "multi_agent", + }, + disable_env_checking=True, + ) + .framework(framework="tf2", eager_tracing=True) + .rollouts( + rollout_fragment_length=rollout_fragment_length, + num_rollout_workers=num_workers, + num_envs_per_worker=1, + enable_tf1_exec_eagerly=True, + ) + .training( + lr_schedule=[(0, 1e-3), (1e3, 5e-4), (1e5, 1e-4), (1e7, 5e-5), (1e8, 1e-5)], + train_batch_size=train_batch_size, + ) + .multi_agent( + policies=rllib_policies, + policy_mapping_fn=lambda agent_id, episode, worker, **kwargs: f"{agent_id}", + ) + .callbacks(callbacks_class=Callbacks) + .debugging(log_level=log_level) ) def get_checkpoint_dir(num): - checkpoint_dir = result_dir / f"checkpoint_{num}" / f"checkpoint-{num}" + checkpoint_dir = Path(result_dir) / f"checkpoint_{num}" / f"checkpoint-{num}" checkpoint_dir.mkdir(parents=True, exist_ok=True) return checkpoint_dir diff --git a/examples/rl/rllib/pg_pbt_example.py b/examples/rl/rllib/pg_pbt_example.py index 4fa50c53d0..b9b49669e2 100644 --- a/examples/rl/rllib/pg_pbt_example.py +++ b/examples/rl/rllib/pg_pbt_example.py @@ -11,7 +11,9 @@ # whether ray[rllib] was installed by user and raises an Exception warning the user to install it if not so. try: from ray import tune - from ray.rllib.algorithms.callbacks import DefaultCallbacks, make_multi_callbacks + from ray.rllib.algorithms.algorithm import AlgorithmConfig + from ray.rllib.algorithms.callbacks import DefaultCallbacks + from ray.rllib.algorithms.pg import PGConfig from ray.rllib.env.base_env import BaseEnv from ray.rllib.evaluation.episode import Episode from ray.rllib.evaluation.episode_v2 import EpisodeV2 @@ -32,10 +34,10 @@ from smarts.sstudio.scenario_construction import build_scenario if __name__ == "__main__": - from configs import gen_parser, gen_pg_config + from configs import gen_parser from rllib_agent import TrainingModel, rllib_agent else: - from .configs import gen_parser, gen_pg_config + from .configs import gen_parser from .rllib_agent import TrainingModel, rllib_agent logging.basicConfig(level=logging.INFO) @@ -152,17 +154,37 @@ def main( agent_specs = agent_values["agent_specs"] smarts.core.seed(seed) - algo_config = gen_pg_config( - scenario=scenario, - envision=envision, - rollout_fragment_length=rollout_fragment_length, - train_batch_size=train_batch_size, - num_workers=max(num_workers, 1), - log_level=log_level, - seed=seed, - callbacks=make_multi_callbacks([Callbacks]), - rllib_policies=rllib_policies, - agent_specs=agent_specs, + assert len(set(rllib_policies.keys()).difference(agent_specs)) == 0 + algo_config: AlgorithmConfig = ( + PGConfig() + .environment( + env=RLlibHiWayEnv, + env_config={ + "seed": seed, + "scenarios": [str(Path(scenario).expanduser().resolve().absolute())], + "headless": not envision, + "agent_specs": agent_specs, + "observation_options": "multi_agent", + }, + disable_env_checking=True, + ) + .framework(framework="tf2", eager_tracing=True) + .rollouts( + rollout_fragment_length=rollout_fragment_length, + num_rollout_workers=num_workers, + num_envs_per_worker=1, + enable_tf1_exec_eagerly=True, + ) + .training( + lr_schedule=[(0, 1e-3), (1e3, 5e-4), (1e5, 1e-4), (1e7, 5e-5), (1e8, 1e-5)], + train_batch_size=train_batch_size, + ) + .multi_agent( + policies=rllib_policies, + policy_mapping_fn=lambda agent_id, episode, worker, **kwargs: f"{agent_id}", + ) + .callbacks(callbacks_class=Callbacks) + .debugging(log_level=log_level) ) experiment_name = "rllib_example_multi" From fcd1b2c924264647fad4d673ed0b77db1dd01836 Mon Sep 17 00:00:00 2001 From: Montgomery Alban Date: Wed, 21 Jun 2023 15:31:49 +0000 Subject: [PATCH 09/16] Update rllib examples with multi-scenario support. --- examples/rl/rllib/configs.py | 14 ++-- examples/rl/rllib/pg_example.py | 19 +++-- examples/rl/rllib/pg_pbt_example.py | 69 +++++++++++-------- examples/tests/test_examples.py | 4 +- examples/tools/argument_parser.py | 4 +- .../cross_rl_agent/train/run_test.py | 4 +- .../cross_rl_agent/train/run_train.py | 4 +- 7 files changed, 69 insertions(+), 49 deletions(-) diff --git a/examples/rl/rllib/configs.py b/examples/rl/rllib/configs.py index 48ab2853de..fb7f1076a3 100644 --- a/examples/rl/rllib/configs.py +++ b/examples/rl/rllib/configs.py @@ -6,22 +6,18 @@ def gen_parser(prog: str, default_result_dir: str) -> argparse.ArgumentParser: parser = argparse.ArgumentParser(prog) parser.add_argument( - "--scenario", + "scenarios", + help="A list of scenarios. Each element can be either the scenario to" + "run or a directory of scenarios to sample from. See `scenarios/`" + "folder for some samples you can use.", type=str, - default=str(Path(__file__).resolve().parents[3] / "scenarios/sumo/loop"), - help="Scenario to run (see scenarios/ for some samples you can use)", + nargs="*", ) parser.add_argument( "--envision", action="store_true", help="Run simulation with Envision display.", ) - parser.add_argument( - "--rollout_fragment_length", - type=str, - default="auto", - help="Episodes are divided into fragments of this many steps for each rollout. In this example this will be ensured to be `1= 0, f"{train_batch_size.__name__} cannot be less than 1." + if isinstance(rollout_fragment_length, str) and rollout_fragment_length != "auto": + rollout_fragment_length = int(rollout_fragment_length) if ( isinstance(rollout_fragment_length, int) and rollout_fragment_length > train_batch_size @@ -161,7 +159,10 @@ def main( env=RLlibHiWayEnv, env_config={ "seed": seed, - "scenarios": [str(Path(scenario).expanduser().resolve().absolute())], + "scenarios": [ + str(Path(scenario).expanduser().resolve().absolute()) + for scenario in scenarios + ], "headless": not envision, "agent_specs": agent_specs, "observation_options": "multi_agent", @@ -183,26 +184,28 @@ def main( policies=rllib_policies, policy_mapping_fn=lambda agent_id, episode, worker, **kwargs: f"{agent_id}", ) - .callbacks(callbacks_class=Callbacks) + .callbacks(callbacks_class=AlgorithmCallbacks) .debugging(log_level=log_level) ) + print(algo_config.to_dict()) + exit(0) experiment_name = "rllib_example_multi" result_dir = Path(result_dir).expanduser().resolve().absolute() experiment_dir = result_dir / experiment_name print(f"======= Checkpointing at {str(result_dir)} =======") + # Note that PBT modifies the hyperparameters during the run. This perturbation can be applied + # to nearly anything passed to `Tuner(param_space=)`. pbt = PopulationBasedTraining( time_attr="time_total_s", metric="episode_reward_mean", mode="max", - perturbation_interval=300, + perturbation_interval=20, resample_probability=0.25, # Specifies the mutations of these hyperparams - # See: `ray.rllib.agents.trainer.COMMON_CONFIG` for common hyperparams hyperparam_mutations={ "lr": [1e-3, 5e-4, 1e-4, 5e-5, 1e-5], - "rollout_fragment_length": lambda: rollout_fragment_length, "train_batch_size": lambda: train_batch_size, }, # Specifies additional mutations after hyperparam_mutations is applied @@ -214,7 +217,7 @@ def main( run_config = air.RunConfig( name=experiment_name, stop={"time_total_s": time_total_s}, - callbacks=[TrialCallback()], + callbacks=[ExperimentCallback()], storage_path=str(result_dir), checkpoint_config=air.CheckpointConfig( num_to_keep=3, @@ -268,8 +271,8 @@ def main( parser.add_argument( "--num_samples", type=int, - default=1, - help="Number of times to sample from hyperparameter space", + default=2, + help="Number of times to sample from hyperparameter space.", ) parser.add_argument( "--save_model_path", @@ -277,11 +280,23 @@ def main( default=str(Path(__file__).expanduser().resolve().parent / "model"), help="Destination path of where to copy the model when training is over", ) + parser.add_argument( + "--rollout_fragment_length", + type=str, + default="auto", + help="Episodes are divided into fragments of this many steps for each rollout. In this example this will be ensured to be `1= Date: Wed, 21 Jun 2023 15:34:54 +0000 Subject: [PATCH 10/16] Add default scenario if scenarios not specified. --- examples/rl/rllib/pg_example.py | 4 ++++ examples/rl/rllib/pg_pbt_example.py | 6 ++++-- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/examples/rl/rllib/pg_example.py b/examples/rl/rllib/pg_example.py index b212f65201..86e4c83c4c 100644 --- a/examples/rl/rllib/pg_example.py +++ b/examples/rl/rllib/pg_example.py @@ -201,6 +201,10 @@ def get_checkpoint_dir(num): help="Episodes are divided into fragments of this many steps for each rollout. In this example this will be ensured to be `1= Date: Wed, 21 Jun 2023 15:35:06 +0000 Subject: [PATCH 11/16] Fix learning test. --- examples/tests/test_learning.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/examples/tests/test_learning.py b/examples/tests/test_learning.py index 4547750907..c5223d6dc2 100644 --- a/examples/tests/test_learning.py +++ b/examples/tests/test_learning.py @@ -21,7 +21,7 @@ def test_learning_regression_rllib(): - from examples.rllib.rllib_agent import TrainingModel, rllib_agent + from examples.rl.rllib.rllib_agent import TrainingModel, rllib_agent ModelCatalog.register_custom_model(TrainingModel.NAME, TrainingModel) rllib_policies = { @@ -37,7 +37,8 @@ def test_learning_regression_rllib(): scenario_path = Path(__file__).parents[2] / "scenarios/sumo/loop" scenario_path = str(scenario_path.absolute()) - tune_confg = { + tune_config = { + "disable_env_checking":True, "env": RLlibHiWayEnv, "env_config": { "scenarios": [scenario_path], @@ -47,8 +48,9 @@ def test_learning_regression_rllib(): }, "multiagent": { "policies": rllib_policies, - "policy_mapping_fn": lambda _: "policy", + "policy_mapping_fn": lambda agent_id, episode, worker, **kwargs: "policy", }, + "framework": "tf2", "log_level": "WARN", "num_workers": multiprocessing.cpu_count() - 1, "horizon": HORIZON, @@ -58,9 +60,9 @@ def test_learning_regression_rllib(): "PPO", name="learning_regression_test", stop={"training_iteration": 60}, - max_failures=10, + max_failures=0, local_dir=make_dir_in_smarts_log_dir("smarts_learning_regression"), - config=tune_confg, + config=tune_config, ) df = analysis.dataframe() From 4c2a0ef25a828178d858a9b724c1f817bc0a707f Mon Sep 17 00:00:00 2001 From: Montgomery Alban Date: Wed, 21 Jun 2023 15:35:26 +0000 Subject: [PATCH 12/16] Update regression rllib. --- examples/tools/regression_rllib.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/examples/tools/regression_rllib.py b/examples/tools/regression_rllib.py index 2986c0db7d..8459b61881 100644 --- a/examples/tools/regression_rllib.py +++ b/examples/tools/regression_rllib.py @@ -24,7 +24,7 @@ from smarts.env.rllib_hiway_env import RLlibHiWayEnv from smarts.zoo.agent_spec import AgentSpec -from ..rllib.rllib_agent import TrainingModel +from ..rl.rllib.rllib_agent import TrainingModel HORIZON = 5000 @@ -100,7 +100,8 @@ def run_experiment(log_path, experiment_name, training_iteration=100): scenario_path = Path(__file__).parent / "../../scenarios/sumo/loop" scenario_path = str(scenario_path.absolute()) - tune_confg = { + tune_config = { + "disable_env_checking":True, "env": RLlibHiWayEnv, "env_config": { "scenarios": [scenario_path], @@ -110,7 +111,7 @@ def run_experiment(log_path, experiment_name, training_iteration=100): }, "multiagent": { "policies": rllib_policies, - "policy_mapping_fn": lambda _: "policy", + "policy_mapping_fn": lambda agent_id, episode, worker, **kwargs: "policy", }, "log_level": "WARN", "num_workers": multiprocessing.cpu_count() - 1, @@ -123,7 +124,7 @@ def run_experiment(log_path, experiment_name, training_iteration=100): stop={"training_iteration": training_iteration}, max_failures=10, local_dir=log_path, - config=tune_confg, + config=tune_config, ) return analysis From 5e4aa9177dfd0b5213e541ac1e31c77f159cee36 Mon Sep 17 00:00:00 2001 From: Montgomery Alban Date: Wed, 21 Jun 2023 17:15:03 +0000 Subject: [PATCH 13/16] Fix pg example main call. --- examples/rl/rllib/pg_example.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/rl/rllib/pg_example.py b/examples/rl/rllib/pg_example.py index 86e4c83c4c..4b9df63d6e 100644 --- a/examples/rl/rllib/pg_example.py +++ b/examples/rl/rllib/pg_example.py @@ -208,7 +208,7 @@ def get_checkpoint_dir(num): build_scenarios(scenarios=args.scenarios, clean=False, seed=args.seed) main( - scenario=args.scenarios, + scenarios=args.scenarios, envision=args.envision, time_total_s=args.time_total_s, rollout_fragment_length=args.rollout_fragment_length, From 2de0d95db41f88c719ab9ad756d0d45bef15b8ca Mon Sep 17 00:00:00 2001 From: Montgomery Alban Date: Wed, 21 Jun 2023 17:15:36 +0000 Subject: [PATCH 14/16] Make format. --- examples/tests/test_learning.py | 2 +- examples/tools/regression_rllib.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/tests/test_learning.py b/examples/tests/test_learning.py index c5223d6dc2..c426cad850 100644 --- a/examples/tests/test_learning.py +++ b/examples/tests/test_learning.py @@ -38,7 +38,7 @@ def test_learning_regression_rllib(): scenario_path = str(scenario_path.absolute()) tune_config = { - "disable_env_checking":True, + "disable_env_checking": True, "env": RLlibHiWayEnv, "env_config": { "scenarios": [scenario_path], diff --git a/examples/tools/regression_rllib.py b/examples/tools/regression_rllib.py index 8459b61881..a51f5cf4d5 100644 --- a/examples/tools/regression_rllib.py +++ b/examples/tools/regression_rllib.py @@ -101,7 +101,7 @@ def run_experiment(log_path, experiment_name, training_iteration=100): scenario_path = str(scenario_path.absolute()) tune_config = { - "disable_env_checking":True, + "disable_env_checking": True, "env": RLlibHiWayEnv, "env_config": { "scenarios": [scenario_path], From 7de417e3a9f7a31f63733592fe8db48baf222603 Mon Sep 17 00:00:00 2001 From: Montgomery Alban Date: Wed, 21 Jun 2023 18:09:32 +0000 Subject: [PATCH 15/16] Fix test learning. --- examples/tests/ci95_reward_lo | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/tests/ci95_reward_lo b/examples/tests/ci95_reward_lo index ce6c66014b..fc004cb52c 100644 --- a/examples/tests/ci95_reward_lo +++ b/examples/tests/ci95_reward_lo @@ -1 +1 @@ -120.0 \ No newline at end of file +18.0 \ No newline at end of file From 62a72d98037d9944c8b6c5119b6d0963af70cdee Mon Sep 17 00:00:00 2001 From: Montgomery Alban Date: Thu, 22 Jun 2023 14:43:21 +0000 Subject: [PATCH 16/16] Final changes for merge. --- CHANGELOG.md | 1 + examples/rl/rllib/pg_example.py | 26 +++++++----------- examples/rl/rllib/pg_pbt_example.py | 41 ++++++++++++----------------- examples/rl/rllib/rllib_agent.py | 5 ++-- smarts/env/custom_observations.py | 3 +-- smarts/env/rllib_hiway_env.py | 4 +-- 6 files changed, 33 insertions(+), 47 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index d5d39833eb..111a9076ae 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -17,6 +17,7 @@ Copy and pasting the git commit messages is __NOT__ enough. - Introduced `tensorflow-probability` to `smarts[rllib]`. - Updated `RLlibHiWayEnv` to use the `gymnasium` interface. - Renamed `rllib/rllib.py` to `rllib/pg_pbt_example.py`. +- Loosened constraint of `gymnasium` from `==0.27.0` to `>=0.26.3`. ### Deprecated ### Fixed - Missing neighborhood vehicle state `'lane_id'` is now added to the `hiway-v1` formatted observations. diff --git a/examples/rl/rllib/pg_example.py b/examples/rl/rllib/pg_example.py index 4b9df63d6e..81265321ff 100644 --- a/examples/rl/rllib/pg_example.py +++ b/examples/rl/rllib/pg_example.py @@ -92,22 +92,16 @@ def main( checkpoint_num: Optional[int], log_level: Literal["DEBUG", "INFO", "WARN", "ERROR"], ): - agent_values = { - "agent_specs": { - f"AGENT-{i}": rllib_agent["agent_spec"] for i in range(num_agents) - }, - "rllib_policies": { - f"AGENT-{i}": ( - None, - rllib_agent["observation_space"], - rllib_agent["action_space"], - {"model": {"custom_model": TrainingModel.NAME}}, - ) - for i in range(num_agents) - }, + rllib_policies = { + f"AGENT-{i}": ( + None, + rllib_agent["observation_space"], + rllib_agent["action_space"], + {"model": {"custom_model": TrainingModel.NAME}}, + ) + for i in range(num_agents) } - rllib_policies = agent_values["rllib_policies"] - agent_specs = agent_values["agent_specs"] + agent_specs = {f"AGENT-{i}": rllib_agent["agent_spec"] for i in range(num_agents)} smarts.core.seed(seed) assert len(set(rllib_policies.keys()).difference(agent_specs)) == 0 @@ -162,7 +156,7 @@ def get_checkpoint_dir(num): algo = algo_config.build() if checkpoint is not None: - Algorithm.load_checkpoint(algo, checkpoint=checkpoint) + algo.load_checkpoint(checkpoint=checkpoint) result = {} current_iteration = 0 checkpoint_iteration = checkpoint_num or 0 diff --git a/examples/rl/rllib/pg_pbt_example.py b/examples/rl/rllib/pg_pbt_example.py index 0fca697e90..0b7a391f63 100644 --- a/examples/rl/rllib/pg_pbt_example.py +++ b/examples/rl/rllib/pg_pbt_example.py @@ -45,7 +45,7 @@ register_env("rllib_hiway-v0", RLlibHiWayEnv) # Add custom metrics to your tensorboard using these callbacks -# See: https://ray.readthedocs.io/en/latest/rllib-training.html#callbacks-and-custom-metrics +# See: https://docs.ray.io/en/latest/rllib/rllib-examples.html#training-workflows class AlgorithmCallbacks(DefaultCallbacks): @staticmethod def on_episode_start( @@ -56,7 +56,7 @@ def on_episode_start( env_index: int, **kwargs, ): - episode.user_data["ego_speed"] = [] + episode.user_data["ego_reward"] = [] @staticmethod def on_episode_step( @@ -69,7 +69,7 @@ def on_episode_step( single_agent_id = list(episode.get_agents())[0] infos = episode._last_infos.get(single_agent_id) if infos is not None: - episode.user_data["ego_speed"].append(infos["speed"]) + episode.user_data["ego_reward"].append(infos["reward"]) @staticmethod def on_episode_end( @@ -81,20 +81,19 @@ def on_episode_end( **kwargs, ): - mean_ego_speed = np.mean(episode.user_data["ego_speed"]) + mean_ego_reward = np.mean(episode.user_data["ego_reward"]) print( f"ep. {episode.episode_id:<12} ended;" f" length={episode.length:<6}" - f" mean_ego_speed={mean_ego_speed:.2f}" + f" mean_ego_reward={mean_ego_reward:.2f}" ) - episode.custom_metrics["mean_ego_speed"] = mean_ego_speed + episode.custom_metrics["mean_ego_reward"] = mean_ego_reward class ExperimentCallback(Callback): def on_trial_error(self, iteration: int, trials: List[Trial], trial: Trial, **info): - path = Path(trial.local_path) - with open(path.parent / "failed_trial", "wt") as f: - f.write(str(path)) + failed_trial = trial + print(f"Trial has failed after {failed_trial.num_failures} failures.") return super().on_trial_error(iteration, trials, trial, **info) @@ -134,22 +133,16 @@ def main( ): rollout_fragment_length = train_batch_size - agent_values = { - "agent_specs": { - f"AGENT-{i}": rllib_agent["agent_spec"] for i in range(num_agents) - }, - "rllib_policies": { - f"AGENT-{i}": ( - None, - rllib_agent["observation_space"], - rllib_agent["action_space"], - {"model": {"custom_model": TrainingModel.NAME}}, - ) - for i in range(num_agents) - }, + rllib_policies = { + f"AGENT-{i}": ( + None, + rllib_agent["observation_space"], + rllib_agent["action_space"], + {"model": {"custom_model": TrainingModel.NAME}}, + ) + for i in range(num_agents) } - rllib_policies = agent_values["rllib_policies"] - agent_specs = agent_values["agent_specs"] + agent_specs = {f"AGENT-{i}": rllib_agent["agent_spec"] for i in range(num_agents)} smarts.core.seed(seed) assert len(set(rllib_policies.keys()).difference(agent_specs)) == 0 diff --git a/examples/rl/rllib/rllib_agent.py b/examples/rl/rllib/rllib_agent.py index d71376f431..ebe0a52062 100644 --- a/examples/rl/rllib/rllib_agent.py +++ b/examples/rl/rllib/rllib_agent.py @@ -42,6 +42,7 @@ } ) +# The FullyConnectedNetwork expects a flattened space. FLATTENED_OBSERVATION_SPACE = gym.spaces.utils.flatten_space(OBSERVATION_SPACE) @@ -70,9 +71,7 @@ def __init__( super().__init__(obs_space, action_space, num_outputs, model_config, name) def forward(self, input_dict, state, seq_lens): - # return super().forward(input_dict, state, seq_lens) - model_out, self._value_out = self.base_model(input_dict["obs_flat"]) - return model_out, state + return super().forward(input_dict, state, seq_lens) ModelCatalog.register_custom_model(TrainingModel.NAME, TrainingModel) diff --git a/smarts/env/custom_observations.py b/smarts/env/custom_observations.py index b876457999..f22b95fe15 100644 --- a/smarts/env/custom_observations.py +++ b/smarts/env/custom_observations.py @@ -180,8 +180,6 @@ def lane_ttc(obs: Union[Observation, Dict]) -> Dict[str, np.ndarray]: wps = [path[0] for path in waypoint_paths] neighborhood_vehicle_states = obs.neighborhood_vehicle_states - # distance of vehicle from center of lane - elif isinstance(obs, dict): waypoint_paths = obs["waypoint_paths"] waypoint_paths = [ @@ -238,6 +236,7 @@ def lane_ttc(obs: Union[Observation, Dict]) -> Dict[str, np.ndarray]: else: raise NotImplementedError("Cannot generate using given observations") + # distance of vehicle from center of lane closest_wp = min(wps, key=lambda wp: wp.dist_to(ego.position)) signed_dist_from_center = closest_wp.signed_lateral_error(ego.position) lane_half_width = closest_wp.lane_width * 0.5 diff --git a/smarts/env/rllib_hiway_env.py b/smarts/env/rllib_hiway_env.py index 8d1397e96b..20f886a7be 100644 --- a/smarts/env/rllib_hiway_env.py +++ b/smarts/env/rllib_hiway_env.py @@ -153,7 +153,6 @@ def __init__(self, config): ) self._smarts = None # Created on env.setup() self._dones_registered = 0 - self._config = config def step(self, agent_actions): """Environment step""" @@ -233,7 +232,8 @@ def step(self, agent_actions): def reset(self, *, seed=None, options=None): """Environment reset.""" - smarts.core.seed(self._seed + (seed or 0)) + if seed not in (None, 0): + smarts.core.seed(self._seed + (seed or 0)) scenario = next(self._scenarios_iterator)

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