Source code for ray.rllib.env.multi_agent_env

import gym
import logging
from typing import Callable, Dict, List, Tuple, Type, Optional, Union, Set

from ray.rllib.env.base_env import BaseEnv
from ray.rllib.env.env_context import EnvContext
from ray.rllib.utils.annotations import ExperimentalAPI, override, PublicAPI, \
    DeveloperAPI
from ray.rllib.utils.typing import AgentID, EnvID, EnvType, MultiAgentDict, \
    MultiEnvDict

# If the obs space is Dict type, look for the global state under this key.
ENV_STATE = "state"

logger = logging.getLogger(__name__)


@PublicAPI
class MultiAgentEnv(gym.Env):
    """An environment that hosts multiple independent agents.

    Agents are identified by (string) agent ids. Note that these "agents" here
    are not to be confused with RLlib Trainers, which are also sometimes
    referred to as "agents" or "RL agents".
    """

    def __init__(self):
        if not hasattr(self, "observation_space"):
            self.observation_space = None
        if not hasattr(self, "action_space"):
            self.action_space = None
        if not hasattr(self, "_agent_ids"):
            self._agent_ids = set()

        # do the action and observation spaces map from agent ids to spaces
        # for the individual agents?
        if not hasattr(self, "_spaces_in_preferred_format"):
            self._spaces_in_preferred_format = None

    @PublicAPI
    def reset(self) -> MultiAgentDict:
        """Resets the env and returns observations from ready agents.

        Returns:
            New observations for each ready agent.

        Examples:
            >>> env = MyMultiAgentEnv()
            >>> obs = env.reset()
            >>> print(obs)
            {
                "car_0": [2.4, 1.6],
                "car_1": [3.4, -3.2],
                "traffic_light_1": [0, 3, 5, 1],
            }
        """
        raise NotImplementedError

    @PublicAPI
    def step(
            self, action_dict: MultiAgentDict
    ) -> Tuple[MultiAgentDict, MultiAgentDict, MultiAgentDict, MultiAgentDict]:
        """Returns observations from ready agents.

        The returns are dicts mapping from agent_id strings to values. The
        number of agents in the env can vary over time.

        Returns:
            Tuple containing 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__" (required) is used to indicate env termination.
            4) Optional info values for each agent id.

        Examples:
            >>> obs, rewards, dones, infos = env.step(
            ...    action_dict={
            ...        "car_0": 1, "car_1": 0, "traffic_light_1": 2,
            ...    })
            >>> print(rewards)
            {
                "car_0": 3,
                "car_1": -1,
                "traffic_light_1": 0,
            }
            >>> print(dones)
            {
                "car_0": False,    # car_0 is still running
                "car_1": True,     # car_1 is done
                "__all__": False,  # the env is not done
            }
            >>> print(infos)
            {
                "car_0": {},  # info for car_0
                "car_1": {},  # info for car_1
            }
        """
        raise NotImplementedError

    @ExperimentalAPI
    def observation_space_contains(self, x: MultiAgentDict) -> bool:
        """Checks if the observation space contains the given key.

        Args:
            x: Observations to check.

        Returns:
            True if the observation space contains the given all observations
                in x.
        """
        if not hasattr(self, "_spaces_in_preferred_format") or \
                self._spaces_in_preferred_format is None:
            self._spaces_in_preferred_format = \
                self._check_if_space_maps_agent_id_to_sub_space()
        if self._spaces_in_preferred_format:
            return self.observation_space.contains(x)

        logger.warning("observation_space_contains() has not been implemented")
        return True

    @ExperimentalAPI
    def action_space_contains(self, x: MultiAgentDict) -> bool:
        """Checks if the action space contains the given action.

        Args:
            x: Actions to check.

        Returns:
            True if the action space contains all actions in x.
        """
        if not hasattr(self, "_spaces_in_preferred_format") or \
                self._spaces_in_preferred_format is None:
            self._spaces_in_preferred_format = \
                self._check_if_space_maps_agent_id_to_sub_space()
        if self._spaces_in_preferred_format:
            return self.action_space.contains(x)

        logger.warning("action_space_contains() has not been implemented")
        return True

    @ExperimentalAPI
    def action_space_sample(self, agent_ids: list = None) -> MultiAgentDict:
        """Returns a random action for each environment, and potentially each
            agent in that environment.

        Args:
            agent_ids: 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.
        """
        if not hasattr(self, "_spaces_in_preferred_format") or \
                self._spaces_in_preferred_format is None:
            self._spaces_in_preferred_format = \
                self._check_if_space_maps_agent_id_to_sub_space()
        if self._spaces_in_preferred_format:
            if agent_ids is None:
                agent_ids = self.get_agent_ids()
            samples = self.action_space.sample()
            return {agent_id: samples[agent_id] for agent_id in agent_ids}
        logger.warning("action_space_sample() has not been implemented")
        del agent_ids
        return {}

    @ExperimentalAPI
    def observation_space_sample(self, agent_ids: list = None) -> MultiEnvDict:
        """Returns a random observation from the observation space for each
        agent if agent_ids is None, otherwise returns a random observation for
        the agents in agent_ids.

        Args:
            agent_ids: 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.
        """

        if not hasattr(self, "_spaces_in_preferred_format") or \
                self._spaces_in_preferred_format is None:
            self._spaces_in_preferred_format = \
                self._check_if_space_maps_agent_id_to_sub_space()
        if self._spaces_in_preferred_format:
            if agent_ids is None:
                agent_ids = self.get_agent_ids()
            samples = self.observation_space.sample()
            samples = {agent_id: samples[agent_id] for agent_id in agent_ids}
            return samples
        logger.warning("observation_space_sample() has not been implemented")
        del agent_ids
        return {}

    @PublicAPI
    def get_agent_ids(self) -> Set[AgentID]:
        """Returns a set of agent ids in the environment.

        Returns:
            Set of agent ids.
        """
        if not isinstance(self._agent_ids, set):
            self._agent_ids = set(self._agent_ids)
        return self._agent_ids

    @PublicAPI
    def render(self, mode=None) -> None:
        """Tries to render the environment."""

        # By default, do nothing.
        pass

    # yapf: disable
    # __grouping_doc_begin__
    @ExperimentalAPI
    def with_agent_groups(
        self,
        groups: Dict[str, List[AgentID]],
        obs_space: gym.Space = None,
            act_space: gym.Space = None) -> "MultiAgentEnv":
        """Convenience method for grouping together agents in this env.

        An agent group is a list of agent IDs that are mapped to a single
        logical agent. All agents of the group must act at the same time in the
        environment. The grouped agent exposes Tuple action and observation
        spaces that are the concatenated action and obs spaces of the
        individual agents.

        The rewards of all the agents in a group are summed. The individual
        agent rewards are available under the "individual_rewards" key of the
        group info return.

        Agent grouping is required to leverage algorithms such as Q-Mix.

        This API is experimental.

        Args:
            groups: Mapping from group id to a list of the agent ids
                of group members. If an agent id is not present in any group
                value, it will be left ungrouped.
            obs_space: Optional observation space for the grouped
                env. Must be a tuple space.
            act_space: Optional action space for the grouped env.
                Must be a tuple space.

        Examples:
            >>> env = YourMultiAgentEnv(...)
            >>> grouped_env = env.with_agent_groups(env, {
            ...   "group1": ["agent1", "agent2", "agent3"],
            ...   "group2": ["agent4", "agent5"],
            ... })
        """

        from ray.rllib.env.wrappers.group_agents_wrapper import \
            GroupAgentsWrapper
        return GroupAgentsWrapper(self, groups, obs_space, act_space)

    # __grouping_doc_end__
    # yapf: enable

    @PublicAPI
    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.
            """
        from ray.rllib.env.remote_base_env import RemoteBaseEnv
        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 = MultiAgentEnvWrapper(
                make_env=make_env, existing_envs=[self], num_envs=num_envs)

        return env

    @DeveloperAPI
    def _check_if_space_maps_agent_id_to_sub_space(self) -> bool:
        # do the action and observation spaces map from agent ids to spaces
        # for the individual agents?
        obs_space_check = (
            hasattr(self, "observation_space")
            and isinstance(self.observation_space, gym.spaces.Dict)
            and set(self.observation_space.keys()) == self.get_agent_ids())
        action_space_check = (
            hasattr(self, "action_space")
            and isinstance(self.action_space, gym.spaces.Dict)
            and set(self.action_space.keys()) == self.get_agent_ids())
        return obs_space_check and action_space_check


[docs]def make_multi_agent( env_name_or_creator: Union[str, Callable[[EnvContext], EnvType]], ) -> Type["MultiAgentEnv"]: """Convenience wrapper for any single-agent env to be converted into MA. Allows you to convert a simple (single-agent) `gym.Env` class into a `MultiAgentEnv` class. This function simply stacks n instances of the given ```gym.Env``` class into one unified ``MultiAgentEnv`` class and returns this class, thus pretending the agents act together in the same environment, whereas - under the hood - they live separately from each other in n parallel single-agent envs. Agent IDs in the resulting and are int numbers starting from 0 (first agent). Args: env_name_or_creator: String specifier or env_maker function taking an EnvContext object as only arg and returning a gym.Env. Returns: New MultiAgentEnv class to be used as env. The constructor takes a config dict with `num_agents` key (default=1). The rest of the config dict will be passed on to the underlying single-agent env's constructor. Examples: >>> # By gym string: >>> ma_cartpole_cls = make_multi_agent("CartPole-v0") >>> # Create a 2 agent multi-agent cartpole. >>> ma_cartpole = ma_cartpole_cls({"num_agents": 2}) >>> obs = ma_cartpole.reset() >>> print(obs) ... {0: [...], 1: [...]} >>> # By env-maker callable: >>> from ray.rllib.examples.env.stateless_cartpole import \ ... StatelessCartPole >>> ma_stateless_cartpole_cls = make_multi_agent( ... lambda config: StatelessCartPole(config)) >>> # Create a 3 agent multi-agent stateless cartpole. >>> ma_stateless_cartpole = ma_stateless_cartpole_cls( ... {"num_agents": 3}) >>> print(obs) ... {0: [...], 1: [...], 2: [...]} """ class MultiEnv(MultiAgentEnv): def __init__(self, config=None): MultiAgentEnv.__init__(self) config = config or {} num = config.pop("num_agents", 1) if isinstance(env_name_or_creator, str): self.agents = [ gym.make(env_name_or_creator) for _ in range(num) ] else: self.agents = [env_name_or_creator(config) for _ in range(num)] self.dones = set() self.observation_space = self.agents[0].observation_space self.action_space = self.agents[0].action_space self._agent_ids = set(range(num)) @override(MultiAgentEnv) def observation_space_sample(self, agent_ids: list = None) -> MultiAgentDict: if agent_ids is None: agent_ids = list(range(len(self.agents))) obs = { agent_id: self.observation_space.sample() for agent_id in agent_ids } return obs @override(MultiAgentEnv) def action_space_sample(self, agent_ids: list = None) -> MultiAgentDict: if agent_ids is None: agent_ids = list(range(len(self.agents))) actions = { agent_id: self.action_space.sample() for agent_id in agent_ids } return actions @override(MultiAgentEnv) def action_space_contains(self, x: MultiAgentDict) -> bool: if not isinstance(x, dict): return False return all(self.action_space.contains(val) for val in x.values()) @override(MultiAgentEnv) def observation_space_contains(self, x: MultiAgentDict) -> bool: if not isinstance(x, dict): return False return all( self.observation_space.contains(val) for val in x.values()) @override(MultiAgentEnv) def reset(self): self.dones = set() return {i: a.reset() for i, a in enumerate(self.agents)} @override(MultiAgentEnv) def step(self, action_dict): obs, rew, done, info = {}, {}, {}, {} for i, action in action_dict.items(): obs[i], rew[i], done[i], info[i] = self.agents[i].step(action) if done[i]: self.dones.add(i) done["__all__"] = len(self.dones) == len(self.agents) return obs, rew, done, info @override(MultiAgentEnv) def render(self, mode=None): return self.agents[0].render(mode) return MultiEnv
class MultiAgentEnvWrapper(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 instance. 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] self._unwrapped_env = self.envs[0].unwrapped self._agent_ids = self._unwrapped_env.get_agent_ids() @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[MultiEnvDict]: ret = {} if isinstance(env_id, int): env_id = [env_id] if env_id is None: env_id = list(range(len(self.envs))) for idx in env_id: obs = self.env_states[idx].reset() assert isinstance(obs, dict), "Not a multi-agent obs" if obs is not None and idx in self.dones: self.dones.remove(idx) ret[idx] = obs return ret @override(BaseEnv) def get_sub_environments(self, as_dict: bool = False) -> List[EnvType]: if as_dict: return { _id: env_state for _id, env_state in enumerate(self.env_states) } 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() @property @override(BaseEnv) @PublicAPI def observation_space(self) -> gym.spaces.Dict: self.envs[0].observation_space @property @override(BaseEnv) @PublicAPI def action_space(self) -> gym.Space: return self.envs[0].action_space @override(BaseEnv) def observation_space_contains(self, x: MultiEnvDict) -> bool: return all( self.envs[0].observation_space_contains(val) for val in x.values()) @override(BaseEnv) def action_space_contains(self, x: MultiEnvDict) -> bool: return all( self.envs[0].action_space_contains(val) for val in x.values()) @override(BaseEnv) def observation_space_sample(self, agent_ids: list = None) -> MultiEnvDict: return {0: self.envs[0].observation_space_sample(agent_ids)} @override(BaseEnv) def action_space_sample(self, agent_ids: list = None) -> MultiEnvDict: return {0: self.envs[0].action_space_sample(agent_ids)} @override(BaseEnv) def get_agent_ids(self) -> Set[AgentID]: return self._agent_ids class _MultiAgentEnvState: def __init__(self, env: MultiAgentEnv): assert isinstance(env, MultiAgentEnv) self.env = env self.initialized = False self.last_obs = {} self.last_rewards = {} self.last_dones = {"__all__": False} self.last_infos = {} 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