mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
285 lines
9.6 KiB
Python
285 lines
9.6 KiB
Python
from six.moves import queue
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import gym
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import threading
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import uuid
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from typing import Optional
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from ray.rllib.utils.annotations import PublicAPI
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from ray.rllib.utils.typing import EnvActionType, EnvObsType, EnvInfoDict
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@PublicAPI
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class ExternalEnv(threading.Thread):
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"""An environment that interfaces with external agents.
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Unlike simulator envs, control is inverted. The environment queries the
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policy to obtain actions and logs observations and rewards for training.
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This is in contrast to gym.Env, where the algorithm drives the simulation
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through env.step() calls.
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You can use ExternalEnv as the backend for policy serving (by serving HTTP
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requests in the run loop), for ingesting offline logs data (by reading
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offline transitions in the run loop), or other custom use cases not easily
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expressed through gym.Env.
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ExternalEnv supports both on-policy actions (through self.get_action()),
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and off-policy actions (through self.log_action()).
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This env is thread-safe, but individual episodes must be executed serially.
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Attributes:
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action_space (gym.Space): Action space.
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observation_space (gym.Space): Observation space.
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Examples:
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>>> register_env("my_env", lambda config: YourExternalEnv(config))
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>>> trainer = DQNTrainer(env="my_env")
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>>> while True:
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>>> print(trainer.train())
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"""
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@PublicAPI
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def __init__(self,
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action_space: gym.Space,
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observation_space: gym.Space,
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max_concurrent: int = 100):
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"""Initializes an external env.
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Args:
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action_space (gym.Space): Action space of the env.
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observation_space (gym.Space): Observation space of the env.
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max_concurrent (int): Max number of active episodes to allow at
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once. Exceeding this limit raises an error.
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"""
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threading.Thread.__init__(self)
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self.daemon = True
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self.action_space = action_space
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self.observation_space = observation_space
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self._episodes = {}
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self._finished = set()
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self._results_avail_condition = threading.Condition()
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self._max_concurrent_episodes = max_concurrent
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@PublicAPI
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def run(self):
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"""Override this to implement the run loop.
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Your loop should continuously:
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1. Call self.start_episode(episode_id)
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2. Call self.get_action(episode_id, obs)
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-or-
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self.log_action(episode_id, obs, action)
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3. Call self.log_returns(episode_id, reward)
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4. Call self.end_episode(episode_id, obs)
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5. Wait if nothing to do.
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Multiple episodes may be started at the same time.
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"""
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raise NotImplementedError
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@PublicAPI
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def start_episode(self,
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episode_id: Optional[str] = None,
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training_enabled: bool = True) -> str:
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"""Record the start of an episode.
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Args:
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episode_id (Optional[str]): Unique string id for the episode or
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None for it to be auto-assigned and returned.
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training_enabled (bool): Whether to use experiences for this
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episode to improve the policy.
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Returns:
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episode_id (str): Unique string id for the episode.
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"""
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if episode_id is None:
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episode_id = uuid.uuid4().hex
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if episode_id in self._finished:
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raise ValueError(
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"Episode {} has already completed.".format(episode_id))
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if episode_id in self._episodes:
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raise ValueError(
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"Episode {} is already started".format(episode_id))
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self._episodes[episode_id] = _ExternalEnvEpisode(
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episode_id, self._results_avail_condition, training_enabled)
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return episode_id
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@PublicAPI
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def get_action(self, episode_id: str,
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observation: EnvObsType) -> EnvActionType:
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"""Record an observation and get the on-policy action.
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Args:
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episode_id (str): Episode id returned from start_episode().
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observation (obj): Current environment observation.
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Returns:
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action (obj): Action from the env action space.
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"""
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episode = self._get(episode_id)
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return episode.wait_for_action(observation)
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@PublicAPI
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def log_action(self, episode_id: str, observation: EnvObsType,
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action: EnvActionType) -> None:
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"""Record an observation and (off-policy) action taken.
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Args:
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episode_id (str): Episode id returned from start_episode().
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observation (obj): Current environment observation.
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action (obj): Action for the observation.
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"""
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episode = self._get(episode_id)
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episode.log_action(observation, action)
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@PublicAPI
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def log_returns(self,
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episode_id: str,
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reward: float,
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info: EnvInfoDict = None) -> None:
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"""Record returns from the environment.
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The reward will be attributed to the previous action taken by the
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episode. Rewards accumulate until the next action. If no reward is
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logged before the next action, a reward of 0.0 is assumed.
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Args:
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episode_id (str): Episode id returned from start_episode().
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reward (float): Reward from the environment.
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info (dict): Optional info dict.
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"""
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episode = self._get(episode_id)
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episode.cur_reward += reward
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if info:
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episode.cur_info = info or {}
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@PublicAPI
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def end_episode(self, episode_id: str, observation: EnvObsType) -> None:
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"""Record the end of an episode.
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Args:
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episode_id (str): Episode id returned from start_episode().
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observation (obj): Current environment observation.
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"""
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episode = self._get(episode_id)
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self._finished.add(episode.episode_id)
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episode.done(observation)
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def _get(self, episode_id: str) -> "_ExternalEnvEpisode":
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"""Get a started episode or raise an error."""
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if episode_id in self._finished:
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raise ValueError(
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"Episode {} has already completed.".format(episode_id))
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if episode_id not in self._episodes:
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raise ValueError("Episode {} not found.".format(episode_id))
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return self._episodes[episode_id]
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class _ExternalEnvEpisode:
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"""Tracked state for each active episode."""
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def __init__(self,
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episode_id: str,
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results_avail_condition: threading.Condition,
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training_enabled: bool,
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multiagent: bool = False):
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self.episode_id = episode_id
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self.results_avail_condition = results_avail_condition
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self.training_enabled = training_enabled
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self.multiagent = multiagent
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self.data_queue = queue.Queue()
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self.action_queue = queue.Queue()
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if multiagent:
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self.new_observation_dict = None
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self.new_action_dict = None
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self.cur_reward_dict = {}
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self.cur_done_dict = {"__all__": False}
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self.cur_info_dict = {}
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else:
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self.new_observation = None
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self.new_action = None
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self.cur_reward = 0.0
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self.cur_done = False
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self.cur_info = {}
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def get_data(self):
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if self.data_queue.empty():
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return None
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return self.data_queue.get_nowait()
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def log_action(self, observation, action):
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if self.multiagent:
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self.new_observation_dict = observation
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self.new_action_dict = action
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else:
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self.new_observation = observation
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self.new_action = action
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self._send()
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self.action_queue.get(True, timeout=60.0)
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def wait_for_action(self, observation):
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if self.multiagent:
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self.new_observation_dict = observation
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else:
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self.new_observation = observation
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self._send()
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return self.action_queue.get(True, timeout=60.0)
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def done(self, observation):
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if self.multiagent:
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self.new_observation_dict = observation
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self.cur_done_dict = {"__all__": True}
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else:
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self.new_observation = observation
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self.cur_done = True
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self._send()
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def _send(self):
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if self.multiagent:
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if not self.training_enabled:
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for agent_id in self.cur_info_dict:
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self.cur_info_dict[agent_id]["training_enabled"] = False
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item = {
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"obs": self.new_observation_dict,
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"reward": self.cur_reward_dict,
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"done": self.cur_done_dict,
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"info": self.cur_info_dict,
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}
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if self.new_action_dict is not None:
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item["off_policy_action"] = self.new_action_dict
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self.new_observation_dict = None
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self.new_action_dict = None
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self.cur_reward_dict = {}
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else:
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item = {
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"obs": self.new_observation,
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"reward": self.cur_reward,
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"done": self.cur_done,
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"info": self.cur_info,
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}
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if self.new_action is not None:
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item["off_policy_action"] = self.new_action
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self.new_observation = None
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self.new_action = None
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self.cur_reward = 0.0
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if not self.training_enabled:
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item["info"]["training_enabled"] = False
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with self.results_avail_condition:
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self.data_queue.put_nowait(item)
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self.results_avail_condition.notify()
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