Source code for ray.rllib.env.external_env

from six.moves import queue
import gym
import threading
import uuid
from typing import Callable, Tuple, Optional, TYPE_CHECKING

from ray.rllib.env.base_env import BaseEnv
from ray.rllib.utils.annotations import override, PublicAPI
from ray.rllib.utils.typing import EnvActionType, EnvInfoDict, EnvObsType, \
    EnvType, MultiEnvDict

if TYPE_CHECKING:
    from ray.rllib.models.preprocessors import Preprocessor


[docs]@PublicAPI class ExternalEnv(threading.Thread): """An environment that interfaces with external agents. Unlike simulator envs, control is inverted: The environment queries the policy to obtain actions and in return logs observations and rewards for training. This is in contrast to gym.Env, where the algorithm drives the simulation through env.step() calls. You can use ExternalEnv as the backend for policy serving (by serving HTTP requests in the run loop), for ingesting offline logs data (by reading offline transitions in the run loop), or other custom use cases not easily expressed through gym.Env. ExternalEnv supports both on-policy actions (through self.get_action()), and off-policy actions (through self.log_action()). This env is thread-safe, but individual episodes must be executed serially. Examples: >>> register_env("my_env", lambda config: YourExternalEnv(config)) >>> trainer = DQNTrainer(env="my_env") >>> while True: >>> print(trainer.train()) """
[docs] @PublicAPI def __init__(self, action_space: gym.Space, observation_space: gym.Space, max_concurrent: int = 100): """Initializes an ExternalEnv instance. Args: action_space: Action space of the env. observation_space: Observation space of the env. max_concurrent: Max number of active episodes to allow at once. Exceeding this limit raises an error. """ threading.Thread.__init__(self) self.daemon = True self.action_space = action_space self.observation_space = observation_space self._episodes = {} self._finished = set() self._results_avail_condition = threading.Condition() self._max_concurrent_episodes = max_concurrent
[docs] @PublicAPI def run(self): """Override this to implement the run loop. Your loop should continuously: 1. Call self.start_episode(episode_id) 2. Call self.[get|log]_action(episode_id, obs, [action]?) 3. Call self.log_returns(episode_id, reward) 4. Call self.end_episode(episode_id, obs) 5. Wait if nothing to do. Multiple episodes may be started at the same time. """ raise NotImplementedError
[docs] @PublicAPI def start_episode(self, episode_id: Optional[str] = None, training_enabled: bool = True) -> str: """Record the start of an episode. Args: episode_id: Unique string id for the episode or None for it to be auto-assigned and returned. training_enabled: Whether to use experiences for this episode to improve the policy. Returns: Unique string id for the episode. """ if episode_id is None: episode_id = uuid.uuid4().hex if episode_id in self._finished: raise ValueError( "Episode {} has already completed.".format(episode_id)) if episode_id in self._episodes: raise ValueError( "Episode {} is already started".format(episode_id)) self._episodes[episode_id] = _ExternalEnvEpisode( episode_id, self._results_avail_condition, training_enabled) return episode_id
[docs] @PublicAPI def get_action(self, episode_id: str, observation: EnvObsType) -> EnvActionType: """Record an observation and get the on-policy action. Args: episode_id: Episode id returned from start_episode(). observation: Current environment observation. Returns: Action from the env action space. """ episode = self._get(episode_id) return episode.wait_for_action(observation)
[docs] @PublicAPI def log_action(self, episode_id: str, observation: EnvObsType, action: EnvActionType) -> None: """Record an observation and (off-policy) action taken. Args: episode_id: Episode id returned from start_episode(). observation: Current environment observation. action: Action for the observation. """ episode = self._get(episode_id) episode.log_action(observation, action)
[docs] @PublicAPI def log_returns(self, episode_id: str, reward: float, info: Optional[EnvInfoDict] = None) -> None: """Records returns (rewards and infos) from the environment. The reward will be attributed to the previous action taken by the episode. Rewards accumulate until the next action. If no reward is logged before the next action, a reward of 0.0 is assumed. Args: episode_id: Episode id returned from start_episode(). reward: Reward from the environment. info: Optional info dict. """ episode = self._get(episode_id) episode.cur_reward += reward if info: episode.cur_info = info or {}
[docs] @PublicAPI def end_episode(self, episode_id: str, observation: EnvObsType) -> None: """Records the end of an episode. Args: episode_id: Episode id returned from start_episode(). observation: Current environment observation. """ episode = self._get(episode_id) self._finished.add(episode.episode_id) episode.done(observation)
def _get(self, episode_id: str) -> "_ExternalEnvEpisode": """Get a started episode by its ID or raise an error.""" if episode_id in self._finished: raise ValueError( "Episode {} has already completed.".format(episode_id)) if episode_id not in self._episodes: raise ValueError("Episode {} not found.".format(episode_id)) return self._episodes[episode_id]
[docs] def to_base_env( self, make_env: Callable[[int], EnvType] = None, num_envs: int = 1, remote_envs: bool = False, remote_env_batch_wait_ms: int = 0, ) -> "BaseEnv": """Converts an RLlib MultiAgentEnv into a BaseEnv object. The resulting BaseEnv is always vectorized (contains n sub-environments) to support batched forward passes, where n may also be 1. BaseEnv also supports async execution via the `poll` and `send_actions` methods and thus supports external simulators. Args: make_env: A callable taking an int as input (which indicates the number of individual sub-environments within the final vectorized BaseEnv) and returning one individual sub-environment. num_envs: The number of sub-environments to create in the resulting (vectorized) BaseEnv. The already existing `env` will be one of the `num_envs`. remote_envs: Whether each sub-env should be a @ray.remote actor. You can set this behavior in your config via the `remote_worker_envs=True` option. remote_env_batch_wait_ms: The wait time (in ms) to poll remote sub-environments for, if applicable. Only used if `remote_envs` is True. Returns: The resulting BaseEnv object. """ if num_envs != 1: raise ValueError( "External(MultiAgent)Env does not currently support " "num_envs > 1. One way of solving this would be to " "treat your Env as a MultiAgentEnv hosting only one " "type of agent but with several copies.") env = ExternalEnvWrapper(self) return env
class _ExternalEnvEpisode: """Tracked state for each active episode.""" def __init__(self, episode_id: str, results_avail_condition: threading.Condition, training_enabled: bool, multiagent: bool = False): self.episode_id = episode_id self.results_avail_condition = results_avail_condition self.training_enabled = training_enabled self.multiagent = multiagent self.data_queue = queue.Queue() self.action_queue = queue.Queue() if multiagent: self.new_observation_dict = None self.new_action_dict = None self.cur_reward_dict = {} self.cur_done_dict = {"__all__": False} self.cur_info_dict = {} else: self.new_observation = None self.new_action = None self.cur_reward = 0.0 self.cur_done = False self.cur_info = {} def get_data(self): if self.data_queue.empty(): return None return self.data_queue.get_nowait() def log_action(self, observation, action): if self.multiagent: self.new_observation_dict = observation self.new_action_dict = action else: self.new_observation = observation self.new_action = action self._send() self.action_queue.get(True, timeout=60.0) def wait_for_action(self, observation): if self.multiagent: self.new_observation_dict = observation else: self.new_observation = observation self._send() return self.action_queue.get(True, timeout=300.0) def done(self, observation): if self.multiagent: self.new_observation_dict = observation self.cur_done_dict = {"__all__": True} else: self.new_observation = observation self.cur_done = True self._send() def _send(self): if self.multiagent: if not self.training_enabled: for agent_id in self.cur_info_dict: self.cur_info_dict[agent_id]["training_enabled"] = False item = { "obs": self.new_observation_dict, "reward": self.cur_reward_dict, "done": self.cur_done_dict, "info": self.cur_info_dict, } if self.new_action_dict is not None: item["off_policy_action"] = self.new_action_dict self.new_observation_dict = None self.new_action_dict = None self.cur_reward_dict = {} else: item = { "obs": self.new_observation, "reward": self.cur_reward, "done": self.cur_done, "info": self.cur_info, } if self.new_action is not None: item["off_policy_action"] = self.new_action self.new_observation = None self.new_action = None self.cur_reward = 0.0 if not self.training_enabled: item["info"]["training_enabled"] = False with self.results_avail_condition: self.data_queue.put_nowait(item) self.results_avail_condition.notify() class ExternalEnvWrapper(BaseEnv): """Internal adapter of ExternalEnv to BaseEnv.""" def __init__(self, external_env: "ExternalEnv", preprocessor: "Preprocessor" = None): from ray.rllib.env.external_multi_agent_env import \ ExternalMultiAgentEnv self.external_env = external_env self.prep = preprocessor self.multiagent = issubclass(type(external_env), ExternalMultiAgentEnv) self._action_space = external_env.action_space if preprocessor: self._observation_space = preprocessor.observation_space else: self._observation_space = external_env.observation_space external_env.start() @override(BaseEnv) def poll(self) -> Tuple[MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict]: with self.external_env._results_avail_condition: results = self._poll() while len(results[0]) == 0: self.external_env._results_avail_condition.wait() results = self._poll() if not self.external_env.is_alive(): raise Exception("Serving thread has stopped.") limit = self.external_env._max_concurrent_episodes assert len(results[0]) < limit, \ ("Too many concurrent episodes, were some leaked? This " "ExternalEnv was created with max_concurrent={}".format(limit)) return results @override(BaseEnv) def send_actions(self, action_dict: MultiEnvDict) -> None: from ray.rllib.env.base_env import _DUMMY_AGENT_ID if self.multiagent: for env_id, actions in action_dict.items(): self.external_env._episodes[env_id].action_queue.put(actions) else: for env_id, action in action_dict.items(): self.external_env._episodes[env_id].action_queue.put( action[_DUMMY_AGENT_ID]) def _poll(self) -> Tuple[MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict]: from ray.rllib.env.base_env import with_dummy_agent_id all_obs, all_rewards, all_dones, all_infos = {}, {}, {}, {} off_policy_actions = {} for eid, episode in self.external_env._episodes.copy().items(): data = episode.get_data() cur_done = episode.cur_done_dict[ "__all__"] if self.multiagent else episode.cur_done if cur_done: del self.external_env._episodes[eid] if data: if self.prep: all_obs[eid] = self.prep.transform(data["obs"]) else: all_obs[eid] = data["obs"] all_rewards[eid] = data["reward"] all_dones[eid] = data["done"] all_infos[eid] = data["info"] if "off_policy_action" in data: off_policy_actions[eid] = data["off_policy_action"] if self.multiagent: # Ensure a consistent set of keys # rely on all_obs having all possible keys for now. for eid, eid_dict in all_obs.items(): for agent_id in eid_dict.keys(): def fix(d, zero_val): if agent_id not in d[eid]: d[eid][agent_id] = zero_val fix(all_rewards, 0.0) fix(all_dones, False) fix(all_infos, {}) return (all_obs, all_rewards, all_dones, all_infos, off_policy_actions) else: return with_dummy_agent_id(all_obs), \ with_dummy_agent_id(all_rewards), \ with_dummy_agent_id(all_dones, "__all__"), \ with_dummy_agent_id(all_infos), \ with_dummy_agent_id(off_policy_actions) @property @override(BaseEnv) @PublicAPI def observation_space(self) -> gym.spaces.Dict: return self._observation_space @property @override(BaseEnv) @PublicAPI def action_space(self) -> gym.Space: return self._action_space