from typing import Callable, Tuple, Optional, List, Dict, Any, TYPE_CHECKING import ray from ray.rllib.env.external_env import ExternalEnv from ray.rllib.env.external_multi_agent_env import ExternalMultiAgentEnv from ray.rllib.env.multi_agent_env import MultiAgentEnv from ray.rllib.env.vector_env import VectorEnv from ray.rllib.utils.annotations import Deprecated, override, PublicAPI from ray.rllib.utils.typing import AgentID, EnvID, EnvType, MultiAgentDict, \ MultiEnvDict, PartialTrainerConfigDict if TYPE_CHECKING: from ray.rllib.models.preprocessors import Preprocessor ASYNC_RESET_RETURN = "async_reset_return" @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 Attributes: action_space (gym.Space): Action space. This must be defined for single-agent envs. Multi-agent envs can set this to None. observation_space (gym.Space): Observation space. This must be defined for single-agent envs. Multi-agent envs can set this to None. 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, }, ... } """ @staticmethod def 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, policy_config: Optional[PartialTrainerConfigDict] = None, ) -> "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. """ from ray.rllib.env.remote_vector_env import RemoteBaseEnv 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): return env # `env` is not a BaseEnv yet -> Need to convert/vectorize. # MultiAgentEnv (which is a gym.Env). if isinstance(env, MultiAgentEnv): # Sub-environments are ray.remote actors: if remote_envs: env = RemoteBaseEnv( make_env, num_envs, multiagent=True, remote_env_batch_wait_ms=remote_env_batch_wait_ms) # Sub-environments are not ray.remote actors. else: env = _MultiAgentEnvToBaseEnv( make_env=make_env, existing_envs=[env], num_envs=num_envs) # ExternalEnv. elif isinstance(env, ExternalEnv): 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 = _ExternalEnvToBaseEnv(env) # VectorEnv. # Note that all BaseEnvs are also vectorized, but the user may want to # define custom vectorization logic and thus implement a custom # VectorEnv class. elif isinstance(env, VectorEnv): env = _VectorEnvToBaseEnv(env) # Anything else: This usually implies that env is a gym.Env object. 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 = _VectorEnvToBaseEnv(env) # Make sure conversion went well. assert isinstance(env, BaseEnv), env return env @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 @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 @PublicAPI def try_reset(self, env_id: Optional[EnvID] = None) -> Optional[MultiAgentDict]: """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). Returns: The reset (multi-agent) observation dict. None if reset is not supported. """ return None @PublicAPI def get_sub_environments(self) -> List[EnvType]: """Return a reference to the underlying sub environments, if any. Returns: List of the underlying sub environments or []. """ return [] @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 @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() # Fixed agent identifier when there is only the single agent in the env _DUMMY_AGENT_ID = "agent0" 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()} class _ExternalEnvToBaseEnv(BaseEnv): """Internal adapter of ExternalEnv to BaseEnv.""" def __init__(self, external_env: ExternalEnv, preprocessor: "Preprocessor" = None): 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) 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) 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: List["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`. """ 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" if set(infos).difference(set(obs)): 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() class _MultiAgentEnvState: def __init__(self, env: 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 = {} # 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 = {} return self.last_obs