ray/rllib/env/multi_agent_env.py

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

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
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"


@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".
    """

    @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

    @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)

    @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_vector_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

# __grouping_doc_end__
# yapf: enable


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):
            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

        @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 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[MultiEnvDict]:
        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)
        obs = {env_id: obs}
        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
        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