Source code for ray.rllib.env.base_env

import logging
from typing import Callable, Tuple, Optional, List, Dict, Any, TYPE_CHECKING,\
    Union, Set

import gym
import ray
from ray.rllib.utils.annotations import Deprecated, override, PublicAPI
from ray.rllib.utils.typing import AgentID, EnvID, EnvType, MultiAgentDict, \
    MultiEnvDict

if TYPE_CHECKING:
    from ray.rllib.models.preprocessors import Preprocessor
    from ray.rllib.env.external_env import ExternalEnv
    from ray.rllib.env.multi_agent_env import MultiAgentEnv
    from ray.rllib.env.vector_env import VectorEnv

ASYNC_RESET_RETURN = "async_reset_return"

logger = logging.getLogger(__name__)


[docs]@PublicAPI class BaseEnv: """The lowest-level env interface used by RLlib for sampling. BaseEnv models multiple agents executing asynchronously in multiple vectorized sub-environments. A call to `poll()` returns observations from ready agents keyed by their sub-environment ID and agent IDs, and actions for those agents can be sent back via `send_actions()`. All other RLlib supported env types can be converted to BaseEnv. RLlib handles these conversions internally in RolloutWorker, for example: gym.Env => rllib.VectorEnv => rllib.BaseEnv rllib.MultiAgentEnv (is-a gym.Env) => rllib.VectorEnv => rllib.BaseEnv rllib.ExternalEnv => rllib.BaseEnv Examples: >>> env = MyBaseEnv() >>> obs, rewards, dones, infos, off_policy_actions = env.poll() >>> print(obs) { "env_0": { "car_0": [2.4, 1.6], "car_1": [3.4, -3.2], }, "env_1": { "car_0": [8.0, 4.1], }, "env_2": { "car_0": [2.3, 3.3], "car_1": [1.4, -0.2], "car_3": [1.2, 0.1], }, } >>> env.send_actions({ ... "env_0": { ... "car_0": 0, ... "car_1": 1, ... }, ... ... }) >>> obs, rewards, dones, infos, off_policy_actions = env.poll() >>> print(obs) { "env_0": { "car_0": [4.1, 1.7], "car_1": [3.2, -4.2], }, ... } >>> print(dones) { "env_0": { "__all__": False, "car_0": False, "car_1": True, }, ... } """
[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-supported env into a BaseEnv object. Supported types for the `env` arg are gym.Env, BaseEnv, VectorEnv, MultiAgentEnv, ExternalEnv, or ExternalMultiAgentEnv. 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. TODO: Support gym3 environments, which are already vectorized. Args: env: An already existing environment of any supported env type to convert/wrap into a BaseEnv. Supported types are gym.Env, BaseEnv, VectorEnv, MultiAgentEnv, ExternalEnv, and ExternalMultiAgentEnv. 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. policy_config: Optional policy config dict. Returns: The resulting BaseEnv object. """ del make_env, num_envs, remote_envs, remote_env_batch_wait_ms return self
[docs] @PublicAPI def poll(self) -> Tuple[MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict]: """Returns observations from ready agents. All return values are two-level dicts mapping from EnvID to dicts mapping from AgentIDs to (observation/reward/etc..) values. The number of agents and sub-environments may vary over time. Returns: Tuple consisting of 1) New observations for each ready agent. 2) Reward values for each ready agent. If the episode is just started, the value will be None. 3) Done values for each ready agent. The special key "__all__" is used to indicate env termination. 4) Info values for each ready agent. 5) Agents may take off-policy actions. When that happens, there will be an entry in this dict that contains the taken action. There is no need to send_actions() for agents that have already chosen off-policy actions. """ raise NotImplementedError
[docs] @PublicAPI def send_actions(self, action_dict: MultiEnvDict) -> None: """Called to send actions back to running agents in this env. Actions should be sent for each ready agent that returned observations in the previous poll() call. Args: action_dict: Actions values keyed by env_id and agent_id. """ raise NotImplementedError
[docs] @PublicAPI def try_reset(self, env_id: Optional[EnvID] = None ) -> Optional[Union[MultiAgentDict, MultiEnvDict]]: """Attempt to reset the sub-env with the given id or all sub-envs. If the environment does not support synchronous reset, None can be returned here. Args: env_id: The sub-environment's ID if applicable. If None, reset the entire Env (i.e. all sub-environments). Note: A MultiAgentDict is returned when using the deprecated wrapper classes such as `ray.rllib.env.base_env._MultiAgentEnvToBaseEnv`, however for consistency with the poll() method, a `MultiEnvDict` is returned from the new wrapper classes, such as `ray.rllib.env.multi_agent_env.MultiAgentEnvWrapper`. Returns: The reset (multi-agent) observation dict. None if reset is not supported. """ return None
[docs] @PublicAPI def get_sub_environments( self, as_dict: bool = False) -> Union[List[EnvType], dict]: """Return a reference to the underlying sub environments, if any. Args: as_dict: If True, return a dict mapping from env_id to env. Returns: List or dictionary of the underlying sub environments or [] / {}. """ if as_dict: return {} return []
[docs] @PublicAPI def get_agent_ids(self) -> Set[AgentID]: """Return the agent ids for the sub_environment. Returns: All agent ids for each the environment. """ return {_DUMMY_AGENT_ID}
[docs] @PublicAPI def try_render(self, env_id: Optional[EnvID] = None) -> None: """Tries to render the sub-environment with the given id or all. Args: env_id: The sub-environment's ID, if applicable. If None, renders the entire Env (i.e. all sub-environments). """ # By default, do nothing. pass
[docs] @PublicAPI def stop(self) -> None: """Releases all resources used.""" # Try calling `close` on all sub-environments. for env in self.get_sub_environments(): if hasattr(env, "close"): env.close()
@Deprecated(new="get_sub_environments", error=False) def get_unwrapped(self) -> List[EnvType]: return self.get_sub_environments() @PublicAPI @property def observation_space(self) -> gym.Space: """Returns the observation space for each agent. Note: samples from the observation space need to be preprocessed into a `MultiEnvDict` before being used by a policy. Returns: The observation space for each environment. """ raise NotImplementedError @PublicAPI @property def action_space(self) -> gym.Space: """Returns the action space for each agent. Note: samples from the action space need to be preprocessed into a `MultiEnvDict` before being passed to `send_actions`. Returns: The observation space for each environment. """ raise NotImplementedError
[docs] @PublicAPI def action_space_sample(self, agent_id: list = None) -> MultiEnvDict: """Returns a random action for each environment, and potentially each agent in that environment. Args: agent_id: List of agent ids to sample actions for. If None or empty list, sample actions for all agents in the environment. Returns: A random action for each environment. """ logger.warning("action_space_sample() has not been implemented") del agent_id return {}
[docs] @PublicAPI def observation_space_sample(self, agent_id: list = None) -> MultiEnvDict: """Returns a random observation for each environment, and potentially each agent in that environment. Args: agent_id: List of agent ids to sample actions for. If None or empty list, sample actions for all agents in the environment. Returns: A random action for each environment. """ logger.warning("observation_space_sample() has not been implemented") del agent_id return {}
[docs] @PublicAPI def last(self) -> Tuple[MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict]: """Returns the last observations, rewards, and done flags that were returned by the environment. Returns: The last observations, rewards, and done flags for each environment """ logger.warning("last has not been implemented for this environment.") return {}, {}, {}, {}, {}
[docs] @PublicAPI def observation_space_contains(self, x: MultiEnvDict) -> bool: """Checks if the given observation is valid for each environment. Args: x: Observations to check. Returns: True if the observations are contained within their respective spaces. False otherwise. """ self._space_contains(self.observation_space, x)
[docs] @PublicAPI def action_space_contains(self, x: MultiEnvDict) -> bool: """Checks if the given actions is valid for each environment. Args: x: Actions to check. Returns: True if the actions are contained within their respective spaces. False otherwise. """ return self._space_contains(self.action_space, x)
def _space_contains(self, space: gym.Space, x: MultiEnvDict) -> bool: """Check if the given space contains the observations of x. Args: space: The space to if x's observations are contained in. x: The observations to check. Returns: True if the observations of x are contained in space. """ agents = set(self.get_agent_ids()) for multi_agent_dict in x.values(): for agent_id, obs in multi_agent_dict: if (agent_id not in agents) or ( not space[agent_id].contains(obs)): return False return True
# Fixed agent identifier when there is only the single agent in the env _DUMMY_AGENT_ID = "agent0" @Deprecated(new="with_dummy_agent_id", error=False) def _with_dummy_agent_id(env_id_to_values: Dict[EnvID, Any], dummy_id: "AgentID" = _DUMMY_AGENT_ID ) -> MultiEnvDict: return {k: {dummy_id: v} for (k, v) in env_id_to_values.items()} def with_dummy_agent_id(env_id_to_values: Dict[EnvID, Any], dummy_id: "AgentID" = _DUMMY_AGENT_ID) -> MultiEnvDict: return {k: {dummy_id: v} for (k, v) in env_id_to_values.items()} @Deprecated( old="ray.rllib.env.base_env._ExternalEnvToBaseEnv", new="ray.rllib.env.external.ExternalEnvWrapper", error=False) class _ExternalEnvToBaseEnv(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: 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]: 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) @Deprecated( old="ray.rllib.env.base_env._VectorEnvToBaseEnv", new="ray.rllib.env.vector_env.VectorEnvWrapper", error=False) class _VectorEnvToBaseEnv(BaseEnv): """Internal adapter of VectorEnv to BaseEnv. We assume the caller will always send the full vector of actions in each call to send_actions(), and that they call reset_at() on all completed environments before calling send_actions(). """ def __init__(self, vector_env: "VectorEnv"): self.vector_env = vector_env self.action_space = vector_env.action_space self.observation_space = vector_env.observation_space self.num_envs = vector_env.num_envs self.new_obs = None # lazily initialized self.cur_rewards = [None for _ in range(self.num_envs)] self.cur_dones = [False for _ in range(self.num_envs)] self.cur_infos = [None for _ in range(self.num_envs)] @override(BaseEnv) def poll(self) -> Tuple[MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict]: if self.new_obs is None: self.new_obs = self.vector_env.vector_reset() new_obs = dict(enumerate(self.new_obs)) rewards = dict(enumerate(self.cur_rewards)) dones = dict(enumerate(self.cur_dones)) infos = dict(enumerate(self.cur_infos)) self.new_obs = [] self.cur_rewards = [] self.cur_dones = [] self.cur_infos = [] return _with_dummy_agent_id(new_obs), \ _with_dummy_agent_id(rewards), \ _with_dummy_agent_id(dones, "__all__"), \ _with_dummy_agent_id(infos), {} @override(BaseEnv) def send_actions(self, action_dict: MultiEnvDict) -> None: action_vector = [None] * self.num_envs for i in range(self.num_envs): action_vector[i] = action_dict[i][_DUMMY_AGENT_ID] self.new_obs, self.cur_rewards, self.cur_dones, self.cur_infos = \ self.vector_env.vector_step(action_vector) @override(BaseEnv) def try_reset(self, env_id: Optional[EnvID] = None) -> MultiAgentDict: assert env_id is None or isinstance(env_id, int) return {_DUMMY_AGENT_ID: self.vector_env.reset_at(env_id)} @override(BaseEnv) def get_sub_environments(self) -> List[EnvType]: return self.vector_env.get_sub_environments() @override(BaseEnv) def try_render(self, env_id: Optional[EnvID] = None) -> None: assert env_id is None or isinstance(env_id, int) return self.vector_env.try_render_at(env_id) @Deprecated( old="ray.rllib.env.base_env._MultiAgentEnvToBaseEnv", new="ray.rllib.env.multi_agent_env.MultiAgentEnvWrapper", error=False) class _MultiAgentEnvToBaseEnv(BaseEnv): """Internal adapter of MultiAgentEnv to BaseEnv. This also supports vectorization if num_envs > 1. """ def __init__(self, make_env: Callable[[int], EnvType], existing_envs: "MultiAgentEnv", num_envs: int): """Wraps MultiAgentEnv(s) into the BaseEnv API. Args: make_env (Callable[[int], EnvType]): Factory that produces a new MultiAgentEnv intance. Must be defined, if the number of existing envs is less than num_envs. existing_envs (List[MultiAgentEnv]): List of already existing multi-agent envs. num_envs (int): Desired num multiagent envs to have at the end in total. This will include the given (already created) `existing_envs`. """ from ray.rllib.env.multi_agent_env import MultiAgentEnv self.make_env = make_env self.envs = existing_envs self.num_envs = num_envs self.dones = set() while len(self.envs) < self.num_envs: self.envs.append(self.make_env(len(self.envs))) for env in self.envs: assert isinstance(env, MultiAgentEnv) self.env_states = [_MultiAgentEnvState(env) for env in self.envs] @override(BaseEnv) def poll(self) -> Tuple[MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict]: obs, rewards, dones, infos = {}, {}, {}, {} for i, env_state in enumerate(self.env_states): obs[i], rewards[i], dones[i], infos[i] = env_state.poll() return obs, rewards, dones, infos, {} @override(BaseEnv) def send_actions(self, action_dict: MultiEnvDict) -> None: for env_id, agent_dict in action_dict.items(): if env_id in self.dones: raise ValueError("Env {} is already done".format(env_id)) env = self.envs[env_id] obs, rewards, dones, infos = env.step(agent_dict) assert isinstance(obs, dict), "Not a multi-agent obs" assert isinstance(rewards, dict), "Not a multi-agent reward" assert isinstance(dones, dict), "Not a multi-agent return" assert isinstance(infos, dict), "Not a multi-agent info" # Allow `__common__` entry in `infos` for data unrelated with any # agent, but rather with the environment itself. if set(infos).difference(set(obs) | {"__common__"}): raise ValueError("Key set for infos must be a subset of obs: " "{} vs {}".format(infos.keys(), obs.keys())) if "__all__" not in dones: raise ValueError( "In multi-agent environments, '__all__': True|False must " "be included in the 'done' dict: got {}.".format(dones)) if dones["__all__"]: self.dones.add(env_id) self.env_states[env_id].observe(obs, rewards, dones, infos) @override(BaseEnv) def try_reset(self, env_id: Optional[EnvID] = None) -> Optional[MultiAgentDict]: obs = self.env_states[env_id].reset() assert isinstance(obs, dict), "Not a multi-agent obs" if obs is not None and env_id in self.dones: self.dones.remove(env_id) return obs @override(BaseEnv) def get_sub_environments(self) -> List[EnvType]: return [state.env for state in self.env_states] @override(BaseEnv) def try_render(self, env_id: Optional[EnvID] = None) -> None: if env_id is None: env_id = 0 assert isinstance(env_id, int) return self.envs[env_id].render() @Deprecated( old="ray.rllib.env.base_env._MultiAgentEnvState", new="ray.rllib.env.multi_agent_env._MultiAgentEnvState", error=False) class _MultiAgentEnvState: def __init__(self, env: "MultiAgentEnv"): from ray.rllib.env.multi_agent_env import MultiAgentEnv assert isinstance(env, MultiAgentEnv) self.env = env self.initialized = False def poll( self ) -> Tuple[MultiAgentDict, MultiAgentDict, MultiAgentDict, MultiAgentDict]: if not self.initialized: self.reset() self.initialized = True observations = self.last_obs rewards = {} dones = {"__all__": self.last_dones["__all__"]} infos = {"__common__": self.last_infos.get("__common__")} # If episode is done, release everything we have. if dones["__all__"]: rewards = self.last_rewards self.last_rewards = {} dones = self.last_dones self.last_dones = {} self.last_obs = {} infos = self.last_infos self.last_infos = {} # Only release those agents' rewards/dones/infos, whose # observations we have. else: for ag in observations.keys(): if ag in self.last_rewards: rewards[ag] = self.last_rewards[ag] del self.last_rewards[ag] if ag in self.last_dones: dones[ag] = self.last_dones[ag] del self.last_dones[ag] if ag in self.last_infos: infos[ag] = self.last_infos[ag] del self.last_infos[ag] self.last_dones["__all__"] = False return observations, rewards, dones, infos def observe(self, obs: MultiAgentDict, rewards: MultiAgentDict, dones: MultiAgentDict, infos: MultiAgentDict): self.last_obs = obs for ag, r in rewards.items(): if ag in self.last_rewards: self.last_rewards[ag] += r else: self.last_rewards[ag] = r for ag, d in dones.items(): if ag in self.last_dones: self.last_dones[ag] = self.last_dones[ag] or d else: self.last_dones[ag] = d self.last_infos = infos def reset(self) -> MultiAgentDict: self.last_obs = self.env.reset() self.last_rewards = {} self.last_dones = {"__all__": False} self.last_infos = {"__common__": {}} return self.last_obs def convert_to_base_env( env: EnvType, 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-supported env into a BaseEnv object. Supported types for the `env` arg are gym.Env, BaseEnv, VectorEnv, MultiAgentEnv, ExternalEnv, or ExternalMultiAgentEnv. 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. TODO: Support gym3 environments, which are already vectorized. Args: env: An already existing environment of any supported env type to convert/wrap into a BaseEnv. Supported types are gym.Env, BaseEnv, VectorEnv, MultiAgentEnv, ExternalEnv, and ExternalMultiAgentEnv. 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. """ from ray.rllib.env.remote_base_env import RemoteBaseEnv from ray.rllib.env.external_env import ExternalEnv from ray.rllib.env.multi_agent_env import MultiAgentEnv from ray.rllib.env.vector_env import VectorEnv, VectorEnvWrapper if remote_envs and num_envs == 1: raise ValueError("Remote envs only make sense to use if num_envs > 1 " "(i.e. vectorization is enabled).") # Given `env` is already a BaseEnv -> Return as is. if isinstance(env, (BaseEnv, MultiAgentEnv, VectorEnv, ExternalEnv)): return env.to_base_env() # `env` is not a BaseEnv yet -> Need to convert/vectorize. else: # Sub-environments are ray.remote actors: if remote_envs: # Determine, whether the already existing sub-env (could # be a ray.actor) is multi-agent or not. multiagent = ray.get(env._is_multi_agent.remote()) if \ hasattr(env, "_is_multi_agent") else False env = RemoteBaseEnv( make_env, num_envs, multiagent=multiagent, remote_env_batch_wait_ms=remote_env_batch_wait_ms, existing_envs=[env], ) # Sub-environments are not ray.remote actors. else: # Convert gym.Env to VectorEnv ... env = VectorEnv.vectorize_gym_envs( make_env=make_env, existing_envs=[env], num_envs=num_envs, action_space=env.action_space, observation_space=env.observation_space, ) # ... then the resulting VectorEnv to a BaseEnv. env = VectorEnvWrapper(env) # Make sure conversion went well. assert isinstance(env, BaseEnv), env return env