I'm confused about how policy mapping works in configuration

  • Medium: It contributes to significant difficulty to complete my task, but I can work around it.

Hi all,
I have a problem understanding the agent_id here (code form multi_agent_cartpole example ).

# Each policy can have a different configuration (including custom model).
    def gen_policy(i):
        config = {
            "model": {
                "custom_model": ["model1", "model2"][i % 2],
            },
            "gamma": random.choice([0.95, 0.99]),
        }
        return PolicySpec(config=config)

    # Setup PPO with an ensemble of `num_policies` different policies.
    policies = {"policy_{}".format(i): gen_policy(i) for i in range(args.num_policies)}
    policy_ids = list(policies. Keys())

    def policy_mapping_fn(agent_id, episode, worker, **kwargs):
        pol_id = random.choice(policy_ids)
        return pol_id

    config = {
        "env": MultiAgentCartPole,
        "env_config": {
            "num_agents": args.num_agents,
        },
        # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
        "num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
        "num_sgd_iter": 10,
        "multiagent": {
            "policies": policies,
            "policy_mapping_fn": policy_mapping_fn,
        },
        "framework": args.framework,
    }
    stop = {
        "episode_reward_mean": args.stop_reward,
        "timesteps_total": args.stop_timesteps,
        "training_iteration": args.stop_iters,
    }

Where do we define agent_id ? in the environment class constructor? I’m confused about how policy mapping works in config, does it call policy_mapping_fn one by one for each agent ? (so agent_id is an argument or a list ).

I hope I describe my problem clearly, I appreciate if someone can help me :),
I am so confused :sweat:

going throw examples makes me more confused,

for example, here

from gym import spaces
import ray
from ray import tune
from ray.rllib.env.multi_agent_env import MultiAgentEnv
import gym
from ray.rllib.policy.policy import PolicySpec
from ray.rllib.examples.policy.random_policy import RandomPolicy



class BasicMultiAgentMultiSpaces(MultiAgentEnv):
    def __init__(self):
        self.agents = {"agent0", "agent1"}
        self.dones = set()
        # Here i'm replacing the env spaces ie: self.observation_space = gym.spaces.Box(.....) to a multiAgentDict space
        self.observation_space = gym.spaces.Dict({"agent0": gym.spaces.Box(low="-1", high=1, shape=(10,)), "agent1": gym.spaces.Box(low="-1", high=1, shape=(20,))})
        self.action_space = gym.spaces.Dict({"agent0": gym.spaces.Discrete(2), "agent1": gym.spaces.Discrete(3)})
        self._agent_ids = set(self.agents)

        self._spaces_in_preferred_format = True
        super().__init__()

    def reset(self):
        self.dones = set()
        return {i: self.observation_space[i].sample() for i in self.agents}

    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.observation_space[i].sample(), 0.0, False, {}
            if done[i]:
                self.dones.add(i)
        done["__all__"] = len(self.dones) == len(self.agents)
        print("step")
        return obs, rew, done, info

def main():

    tune.register_env(
        "ExampleEnv",
        lambda c: BasicMultiAgentMultiSpaces()
    )
    def policy_mapping_fn(agent_id, episode, worker, **kwargs):
        # Fix here after feedback from sven
        return "main0" if agent_id == "agent0" else "main1"

    ray.init()
    tune.run(
        "PPO",
        stop={"episode_reward_mean": 200},
        config={
            "env": "ExampleEnv",
            "num_gpus": 0,
            "num_workers": 1,
            "multiagent" :{
                "policies": {
                    "main": PolicySpec(),
                    "random": PolicySpec(policy_class=RandomPolicy),
                },
                "policy_mapping_fn": policy_mapping_fn,
                # Fix here
                "policies_to_train": ["main0", "main1"]
            },
            "framework": "torch"
        }
    )

agent ids are defined by self._agent_ids = set(self. Agents) in environment class constructor,
if I comment it (or even change _agent_ids to agent_ids) I get errors.

But here, self._agent_ids is not defined and it works.
https://github.com/ray-project/ray/blob/ed04ab71401874db8b2f4f91ca5737919259b950/rllib/examples/env/multi_agent.py#L31

import gym
import random

from ray.rllib.env.multi_agent_env import MultiAgentEnv, make_multi_agent
from ray.rllib.examples.env.mock_env import MockEnv, MockEnv2
from ray.rllib.examples.env.stateless_cartpole import StatelessCartPole
from ray.rllib.utils.annotations import Deprecated


@Deprecated(
    old="ray.rllib.examples.env.multi_agent.make_multiagent",
    new="ray.rllib.env.multi_agent_env.make_multi_agent",
    error=False)
def make_multiagent(env_name_or_creator):
    return make_multi_agent(env_name_or_creator)


class BasicMultiAgent(MultiAgentEnv):
    """Env of N independent agents, each of which exits after 25 steps."""

    def __init__(self, num):
        self.agents = [MockEnv(25) for _ in range(num)]
        self.dones = set()
        self.observation_space = gym.spaces.Discrete(2)
        self.action_space = gym.spaces.Discrete(2)
        self.resetted = False

    def reset(self):
        self.resetted = True
        self.dones = set()
        return {i: a.reset() for i, a in enumerate(self.agents)}

    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


class EarlyDoneMultiAgent(MultiAgentEnv):
    """Env for testing when the env terminates (after agent 0 does)."""

    def __init__(self):
        self.agents = [MockEnv(3), MockEnv(5)]
        self.dones = set()
        self.last_obs = {}
        self.last_rew = {}
        self.last_done = {}
        self.last_info = {}
        self.i = 0
        self.observation_space = gym.spaces.Discrete(10)
        self.action_space = gym.spaces.Discrete(2)

    def reset(self):
        self.dones = set()
        self.last_obs = {}
        self.last_rew = {}
        self.last_done = {}
        self.last_info = {}
        self.i = 0
        for i, a in enumerate(self.agents):
            self.last_obs[i] = a.reset()
            self.last_rew[i] = None
            self.last_done[i] = False
            self.last_info[i] = {}
        obs_dict = {self.i: self.last_obs[self.i]}
        self.i = (self.i + 1) % len(self.agents)
        return obs_dict

    def step(self, action_dict):
        assert len(self.dones) != len(self.agents)
        for i, action in action_dict.items():
            (self.last_obs[i], self.last_rew[i], self.last_done[i],
             self.last_info[i]) = self.agents[i].step(action)
        obs = {self.i: self.last_obs[self.i]}
        rew = {self.i: self.last_rew[self.i]}
        done = {self.i: self.last_done[self.i]}
        info = {self.i: self.last_info[self.i]}
        if done[self.i]:
            rew[self.i] = 0
            self.dones.add(self.i)
        self.i = (self.i + 1) % len(self.agents)
        done["__all__"] = len(self.dones) == len(self.agents) - 1
        return obs, rew, done, info


class FlexAgentsMultiAgent(MultiAgentEnv):
    """Env of independent agents, each of which exits after n steps."""

    def __init__(self):
        self.agents = {}
        self.agentID = 0
        self.dones = set()
        self.observation_space = gym.spaces.Discrete(2)
        self.action_space = gym.spaces.Discrete(2)
        self.resetted = False

    def spawn(self):
        # Spawn a new agent into the current episode.
        agentID = self.agentID
        self.agents[agentID] = MockEnv(25)
        self.agentID += 1
        return agentID

    def reset(self):
        self.agents = {}
        self.spawn()
        self.resetted = True
        self.dones = set()

        obs = {}
        for i, a in self.agents.items():
            obs[i] = a.reset()

        return obs

    def step(self, action_dict):
        obs, rew, done, info = {}, {}, {}, {}
        # Apply the actions.
        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)

        # Sometimes, add a new agent to the episode.
        if random.random() > 0.75:
            i = self.spawn()
            obs[i], rew[i], done[i], info[i] = self.agents[i].step(action)
            if done[i]:
                self.dones.add(i)

        # Sometimes, kill an existing agent.
        if len(self.agents) > 1 and random.random() > 0.25:
            keys = list(self.agents.keys())
            key = random.choice(keys)
            done[key] = True
            del self.agents[key]

        done["__all__"] = len(self.dones) == len(self.agents)
        return obs, rew, done, info


class RoundRobinMultiAgent(MultiAgentEnv):
    """Env of N independent agents, each of which exits after 5 steps.

    On each step() of the env, only one agent takes an action."""

    def __init__(self, num, increment_obs=False):
        if increment_obs:
            # Observations are 0, 1, 2, 3... etc. as time advances
            self.agents = [MockEnv2(5) for _ in range(num)]
        else:
            # Observations are all zeros
            self.agents = [MockEnv(5) for _ in range(num)]
        self.dones = set()
        self.last_obs = {}
        self.last_rew = {}
        self.last_done = {}
        self.last_info = {}
        self.i = 0
        self.num = num
        self.observation_space = gym.spaces.Discrete(10)
        self.action_space = gym.spaces.Discrete(2)

    def reset(self):
        self.dones = set()
        self.last_obs = {}
        self.last_rew = {}
        self.last_done = {}
        self.last_info = {}
        self.i = 0
        for i, a in enumerate(self.agents):
            self.last_obs[i] = a.reset()
            self.last_rew[i] = None
            self.last_done[i] = False
            self.last_info[i] = {}
        obs_dict = {self.i: self.last_obs[self.i]}
        self.i = (self.i + 1) % self.num
        return obs_dict

    def step(self, action_dict):
        assert len(self.dones) != len(self.agents)
        for i, action in action_dict.items():
            (self.last_obs[i], self.last_rew[i], self.last_done[i],
             self.last_info[i]) = self.agents[i].step(action)
        obs = {self.i: self.last_obs[self.i]}
        rew = {self.i: self.last_rew[self.i]}
        done = {self.i: self.last_done[self.i]}
        info = {self.i: self.last_info[self.i]}
        if done[self.i]:
            rew[self.i] = 0
            self.dones.add(self.i)
        self.i = (self.i + 1) % self.num
        done["__all__"] = len(self.dones) == len(self.agents)
        return obs, rew, done, info


MultiAgentCartPole = make_multi_agent("CartPole-v0")
MultiAgentMountainCar = make_multi_agent("MountainCarContinuous-v0")
MultiAgentPendulum = make_multi_agent("Pendulum-v0")
MultiAgentStatelessCartPole = make_multi_agent(
    lambda config: StatelessCartPole(config))

I read this issue:
https://discuss.ray.io/t/get-agent-id-in-multi-agent-setting/3535

and still, I am confused, should agent ids defined in the constructor, step method or somewhere else?

Hi @Mehdi,

The names of the agents are defined in the environment you provide and are included as keys in the data provided by reset and step.

In RLLIB algorithms there are policies that make the action decisions given observation from the environment. These algorithms are optimized with an RL algorithm during training.

In the RLLIB config you need to define the policies you want to use to make action decisions. If you don’t specify any a single policy called “default_policy” will be created.

You also need to create a policy mapping function that maps agent ids to policy ids. Unless you are using the default_policy in which case you do not need to provide this mapping because they are all mapped to one policy.

Now here is the part I think you are confused by. There is no formal specification of the agent_ids provided during configuration. That is implicit information in the environment that you need to know ahead of time or write some methods in your environment to retrieve them. The member _agent_ids is an attempt to remedy that implicit knowledge but it is an RLLIB convention and most environments do not have that.

You do not necessarily need to know the exact agent names ahead of time if they are named according to some convention. For example perhaps you have an environment that has car agents (whose names are formatted like car_0, car_1, car_2,… ) and bicycles (bike_0, bike_1, …) and you have two policies one for cars (car_policy) and one for bicycles (bike_policy). You could write a policy mapping function like this:

def agent_to_policy_map(agent_id):
    if agent_id.startwith("car"):
        return "car"
    elif agent_id.startswith("bike"):
        return "bike"
    else:
        raise ValueError("Unknown agent type: ", agent_id)
2 Likes

Thanks @mannyv

Yes, you’re right, I have tried more examples and finally got how it works.

Many thanks :blush:

Also here we can see how rllib works:
https://docs.ray.io/en/latest/_modules/ray/rllib/env/multi_agent_env.html

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.utils.annotations import (
    ExperimentalAPI,
    override,
    PublicAPI,
    DeveloperAPI,
)
from ray.rllib.utils.typing import (
    AgentID,
    EnvCreator,
    EnvID,
    EnvType,
    MultiAgentDict,
    MultiEnvDict,
)
from ray.util import log_once

# 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:
            >>> from ray.rllib.env.multi_agent_env import MultiAgentEnv
            >>> class MyMultiAgentEnv(MultiAgentEnv): # doctest: +SKIP
            ...     # Define your env here. # doctest: +SKIP
            ...     ... # doctest: +SKIP
            >>> env = MyMultiAgentEnv() # doctest: +SKIP
            >>> obs = env.reset() # doctest: +SKIP
            >>> print(obs) # doctest: +SKIP
            {
                "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:
            >>> env = ... # doctest: +SKIP
            >>> obs, rewards, dones, infos = env.step( # doctest: +SKIP
            ...    action_dict={ # doctest: +SKIP
            ...        "car_0": 1, "car_1": 0, "traffic_light_1": 2, # doctest: +SKIP
            ...    }) # doctest: +SKIP
            >>> print(rewards) # doctest: +SKIP
            {
                "car_0": 3,
                "car_1": -1,
                "traffic_light_1": 0,
            }
            >>> print(dones) # doctest: +SKIP
            {
                "car_0": False,    # car_0 is still running
                "car_1": True,     # car_1 is done
                "__all__": False,  # the env is not done
            }
            >>> print(infos) # doctest: +SKIP
            {
                "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)

        if log_once("action_space_contains"):
            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
                if agent_id != "__all__"
            }
        logger.warning("action_space_sample() has not been implemented")
        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
        if log_once("observation_space_sample"):
            logger.warning("observation_space_sample() has not been implemented")
        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

    # fmt: off
    # __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:
            >>> from ray.rllib.env.multi_agent_env import MultiAgentEnv
            >>> class MyMultiAgentEnv(MultiAgentEnv): # doctest: +SKIP
            ...     # define your env here
            ...     ... # doctest: +SKIP
            >>> env = MyMultiAgentEnv(...) # doctest: +SKIP
            >>> grouped_env = env.with_agent_groups(env, { # doctest: +SKIP
            ...   "group1": ["agent1", "agent2", "agent3"], # doctest: +SKIP
            ...   "group2": ["agent4", "agent5"], # doctest: +SKIP
            ... }) # doctest: +SKIP
        """

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

    # __grouping_doc_end__
    # fmt: on

    @PublicAPI
    def to_base_env(
        self,
        make_env: Optional[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.spaces.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, EnvCreator],
) -> 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:
         >>> from ray.rllib.env.multi_agent_env import make_multi_agent
         >>> # By gym string:
         >>> ma_cartpole_cls = make_multi_agent("CartPole-v0") # doctest: +SKIP
         >>> # Create a 2 agent multi-agent cartpole.
         >>> ma_cartpole = ma_cartpole_cls({"num_agents": 2}) # doctest: +SKIP
         >>> obs = ma_cartpole.reset() # doctest: +SKIP
         >>> print(obs) # doctest: +SKIP
         {0: [...], 1: [...]}
         >>> # By env-maker callable:
         >>> from ray.rllib.examples.env.stateless_cartpole # doctest: +SKIP
         ...    import StatelessCartPole
         >>> ma_stateless_cartpole_cls = make_multi_agent( # doctest: +SKIP
         ...    lambda config: StatelessCartPole(config)) # doctest: +SKIP
         >>> # Create a 3 agent multi-agent stateless cartpole.
         >>> ma_stateless_cartpole = ma_stateless_cartpole_cls( # doctest: +SKIP
         ...    {"num_agents": 3}) # doctest: +SKIP
         >>> print(obs) # doctest: +SKIP
         {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: List["MultiAgentEnv"],
        num_envs: int,
    ):
        """Wraps MultiAgentEnv(s) into the BaseEnv API.

        Args:
            make_env: Factory that produces a new MultiAgentEnv instance taking the
                vector index as only call argument.
                Must be defined, if the number of existing envs is less than num_envs.
            existing_envs: List of already existing multi-agent envs.
            num_envs: 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

    @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.envs[0].get_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

As I understand, if we use “self._spaces_in_preferred_format = True” in the environment class constructor, we can define our agent ids by “self._agent_ids”.

otherwise, it samples agent ids from our observation space.