multi_agent_env.py

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

import numpy as np

from ray.rllib.env.base_env import BaseEnv
from ray.rllib.env.env_context import EnvContext
from ray.rllib.utils.annotations import OldAPIStack, override
from ray.rllib.utils.deprecation import Deprecated
from ray.rllib.utils.typing import (
    AgentID,
    EnvCreator,
    EnvID,
    EnvType,
    MultiAgentDict,
    MultiEnvDict,
)
from ray.util import log_once
from ray.util.annotations import DeveloperAPI, PublicAPI

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

logger = logging.getLogger(__name__)


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

    Agents are identified by AgentIDs (string).
    """

    # Optional mappings from AgentID to individual agents' spaces.
    # Set this to an "exhaustive" dictionary, mapping all possible AgentIDs to
    # individual agents' spaces. Alternatively, override
    # `get_observation_space(agent_id=...)` and `get_action_space(agent_id=...)`, which
    # is the API that RLlib uses to get individual spaces and whose default
    # implementation is to simply look up `agent_id` in these dicts.
    observation_spaces: Optional[Dict[AgentID, gym.Space]] = None
    action_spaces: Optional[Dict[AgentID, gym.Space]] = None

    # All agents currently active in the environment. This attribute may change during
    # the lifetime of the env or even during an individual episode.
    agents: List[AgentID] = []
    # All agents that may appear in the environment, ever.
    # This attribute should not be changed during the lifetime of this env.
    possible_agents: List[AgentID] = []

    # @OldAPIStack, use `observation_spaces` and `action_spaces`, instead.
    observation_space: Optional[gym.Space] = None
    action_space: Optional[gym.Space] = None

    def __init__(self):
        super().__init__()

        # @OldAPIStack
        if not hasattr(self, "_agent_ids"):
            self._agent_ids = set()

        # If these important attributes are not set, try to infer them.
        if not self.agents:
            self.agents = list(self._agent_ids)
        if not self.possible_agents:
            self.possible_agents = self.agents.copy()

    def reset(
        self,
        *,
        seed: Optional[int] = None,
        options: Optional[dict] = None,
    ) -> Tuple[MultiAgentDict, MultiAgentDict]:  # type: ignore
        """Resets the env and returns observations from ready agents.

        Args:
            seed: An optional seed to use for the new episode.

        Returns:
            New observations for each ready agent.

        .. testcode::
            :skipif: True

            from ray.rllib.env.multi_agent_env import MultiAgentEnv
            class MyMultiAgentEnv(MultiAgentEnv):
                # Define your env here.
            env = MyMultiAgentEnv()
            obs, infos = env.reset(seed=42, options={})
            print(obs)

        .. testoutput::

            {
                "car_0": [2.4, 1.6],
                "car_1": [3.4, -3.2],
                "traffic_light_1": [0, 3, 5, 1],
            }
        """
        # Call super's `reset()` method to (maybe) set the given `seed`.
        super().reset(seed=seed, options=options)

    def step(
        self, action_dict: MultiAgentDict
    ) -> Tuple[
        MultiAgentDict, 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) Terminated values for each ready agent. The special key
            "__all__" (required) is used to indicate env termination.
            4) Truncated values for each ready agent.
            5) Info values for each agent id (may be empty dicts).

        .. testcode::
            :skipif: True

            env = ...
            obs, rewards, terminateds, truncateds, infos = env.step(action_dict={
                "car_0": 1, "car_1": 0, "traffic_light_1": 2,
            })
            print(rewards)

            print(terminateds)

            print(infos)

        .. testoutput::

            {
                "car_0": 3,
                "car_1": -1,
                "traffic_light_1": 0,
            }
            {
                "car_0": False,    # car_0 is still running
                "car_1": True,     # car_1 is terminated
                "__all__": False,  # the env is not terminated
            }
            {
                "car_0": {},  # info for car_0
                "car_1": {},  # info for car_1
            }

        """
        raise NotImplementedError

    def render(self) -> None:
        """Tries to render the environment."""

        # By default, do nothing.
        pass

    def get_observation_space(self, agent_id: AgentID) -> gym.Space:
        if self.observation_spaces is not None:
            return self.observation_spaces[agent_id]

        # @OldAPIStack behavior.
        # `self.observation_space` is a `gym.spaces.Dict` AND contains `agent_id`.
        if (
            isinstance(self.observation_space, gym.spaces.Dict)
            and agent_id in self.observation_space.spaces
        ):
            return self.observation_space[agent_id]
        # `self.observation_space` is not a `gym.spaces.Dict` OR doesn't contain
        # `agent_id` -> The defined space is most likely meant to be the space
        # for all agents.
        else:
            return self.observation_space

    def get_action_space(self, agent_id: AgentID) -> gym.Space:
        if self.action_spaces is not None:
            return self.action_spaces[agent_id]

        # @OldAPIStack behavior.
        # `self.action_space` is a `gym.spaces.Dict` AND contains `agent_id`.
        if (
            isinstance(self.action_space, gym.spaces.Dict)
            and agent_id in self.action_space.spaces
        ):
            return self.action_space[agent_id]
        # `self.action_space` is not a `gym.spaces.Dict` OR doesn't contain
        # `agent_id` -> The defined space is most likely meant to be the space
        # for all agents.
        else:
            return self.action_space

    @property
    def num_agents(self) -> int:
        return len(self.agents)

    @property
    def max_num_agents(self) -> int:
        return len(self.possible_agents)

    # fmt: off
    # __grouping_doc_begin__
    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.

        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. The group id becomes a new agent ID
                in the final environment.
            obs_space: Optional observation space for the grouped
                env. Must be a tuple space. If not provided, will infer this to be a
                Tuple of n individual agents spaces (n=num agents in a group).
            act_space: Optional action space for the grouped env.
                Must be a tuple space. If not provided, will infer this to be a Tuple
                of n individual agents spaces (n=num agents in a group).

        .. testcode::
            :skipif: True

            from ray.rllib.env.multi_agent_env import MultiAgentEnv
            class MyMultiAgentEnv(MultiAgentEnv):
                # define your env here
                ...
            env = MyMultiAgentEnv(...)
            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__
    # fmt: on

    @OldAPIStack
    @Deprecated(new="MultiAgentEnv.possible_agents", error=False)
    def get_agent_ids(self) -> Set[AgentID]:
        if not hasattr(self, "_agent_ids"):
            self._agent_ids = set()
        if not isinstance(self._agent_ids, set):
            self._agent_ids = set(self._agent_ids)
        # Make this backward compatible as much as possible.
        return self._agent_ids if self._agent_ids else set(self.agents)

    @OldAPIStack
    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,
        restart_failed_sub_environments: bool = False,
    ) -> "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.
            restart_failed_sub_environments: If True and any sub-environment (within
                a vectorized env) throws any error during env stepping, we will try to
                restart the faulty sub-environment. This is done
                without disturbing the other (still intact) sub-environments.

        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,
                restart_failed_sub_environments=restart_failed_sub_environments,
            )
        # Sub-environments are not ray.remote actors.
        else:
            env = MultiAgentEnvWrapper(
                make_env=make_env,
                existing_envs=[self],
                num_envs=num_envs,
                restart_failed_sub_environments=restart_failed_sub_environments,
            )

        return env


@DeveloperAPI
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.

    .. testcode::
        :skipif: True

        from ray.rllib.env.multi_agent_env import make_multi_agent
        # By gym string:
        ma_cartpole_cls = make_multi_agent("CartPole-v1")
        # Create a 2 agent multi-agent cartpole.
        ma_cartpole = ma_cartpole_cls({"num_agents": 2})
        obs = ma_cartpole.reset()
        print(obs)

        # By env-maker callable:
        from ray.rllib.examples.envs.classes.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)

    .. testoutput::

        {0: [...], 1: [...]}
        {0: [...], 1: [...], 2: [...]}
    """

    class MultiEnv(MultiAgentEnv):
        def __init__(self, config: EnvContext = None):
            super().__init__()

            # Note: Explicitly check for None here, because config
            # can have an empty dict but meaningful data fields (worker_index,
            # vector_index) etc.
            # TODO (sven): Clean this up, so we are not mixing up dict fields
            #  with data fields.
            if config is None:
                config = {}
            num = config.pop("num_agents", 1)
            if isinstance(env_name_or_creator, str):
                self.envs = [gym.make(env_name_or_creator) for _ in range(num)]
            else:
                self.envs = [env_name_or_creator(config) for _ in range(num)]
            self.terminateds = set()
            self.truncateds = set()
            self.observation_spaces = {
                i: self.envs[i].observation_space for i in range(num)
            }
            self.action_spaces = {i: self.envs[i].action_space for i in range(num)}
            self.agents = list(range(num))
            self.possible_agents = self.agents.copy()

        @override(MultiAgentEnv)
        def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None):
            self.terminateds = set()
            self.truncateds = set()
            obs, infos = {}, {}
            for i, env in enumerate(self.envs):
                obs[i], infos[i] = env.reset(seed=seed, options=options)

            return obs, infos

        @override(MultiAgentEnv)
        def step(self, action_dict):
            obs, rew, terminated, truncated, info = {}, {}, {}, {}, {}

            # The environment is expecting an action for at least one agent.
            if len(action_dict) == 0:
                raise ValueError(
                    "The environment is expecting an action for at least one agent."
                )

            for i, action in action_dict.items():
                obs[i], rew[i], terminated[i], truncated[i], info[i] = self.envs[
                    i
                ].step(action)
                if terminated[i]:
                    self.terminateds.add(i)
                if truncated[i]:
                    self.truncateds.add(i)
            # TODO: Flaw in our MultiAgentEnv API wrt. new gymnasium: Need to return
            #  an additional episode_done bool that covers cases where all agents are
            #  either terminated or truncated, but not all are truncated and not all are
            #  terminated. We can then get rid of the aweful `__all__` special keys!
            terminated["__all__"] = len(self.terminateds) + len(self.truncateds) == len(
                self.envs
            )
            truncated["__all__"] = len(self.truncateds) == len(self.envs)
            return obs, rew, terminated, truncated, info

        @override(MultiAgentEnv)
        def render(self):
            # This render method simply renders all n underlying individual single-agent
            # envs and concatenates their images (on top of each other if the returned
            # images have dims where [width] > [height], otherwise next to each other).
            render_images = [e.render() for e in self.envs]
            if render_images[0].shape[1] > render_images[0].shape[0]:
                concat_dim = 0
            else:
                concat_dim = 1
            return np.concatenate(render_images, axis=concat_dim)

    return MultiEnv


@OldAPIStack
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,
        restart_failed_sub_environments: bool = False,
    ):
        """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`.
            restart_failed_sub_environments: If True and any sub-environment (within
                this vectorized env) throws any error during env stepping, we will try
                to restart the faulty sub-environment. This is done
                without disturbing the other (still intact) sub-environments.
        """
        self.make_env = make_env
        self.envs = existing_envs
        self.num_envs = num_envs
        self.restart_failed_sub_environments = restart_failed_sub_environments

        self.terminateds = set()
        self.truncateds = 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._init_env_state(idx=None)
        self._unwrapped_env = self.envs[0].unwrapped

    @override(BaseEnv)
    def poll(
        self,
    ) -> Tuple[
        MultiEnvDict,
        MultiEnvDict,
        MultiEnvDict,
        MultiEnvDict,
        MultiEnvDict,
        MultiEnvDict,
    ]:
        obs, rewards, terminateds, truncateds, infos = {}, {}, {}, {}, {}
        for i, env_state in enumerate(self.env_states):
            (
                obs[i],
                rewards[i],
                terminateds[i],
                truncateds[i],
                infos[i],
            ) = env_state.poll()
        return obs, rewards, terminateds, truncateds, 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.terminateds or env_id in self.truncateds:
                raise ValueError(
                    f"Env {env_id} is already done and cannot accept new actions"
                )
            env = self.envs[env_id]
            try:
                obs, rewards, terminateds, truncateds, infos = env.step(agent_dict)
            except Exception as e:
                if self.restart_failed_sub_environments:
                    logger.exception(e.args[0])
                    self.try_restart(env_id=env_id)
                    obs = e
                    rewards = {}
                    terminateds = {"__all__": True}
                    truncateds = {"__all__": False}
                    infos = {}
                else:
                    raise e

            assert isinstance(
                obs, (dict, Exception)
            ), "Not a multi-agent obs dict or an Exception!"
            assert isinstance(rewards, dict), "Not a multi-agent reward dict!"
            assert isinstance(terminateds, dict), "Not a multi-agent terminateds dict!"
            assert isinstance(truncateds, dict), "Not a multi-agent truncateds dict!"
            assert isinstance(infos, dict), "Not a multi-agent info dict!"
            if isinstance(obs, dict):
                info_diff = set(infos).difference(set(obs))
                if info_diff and info_diff != {"__common__"}:
                    raise ValueError(
                        "Key set for infos must be a subset of obs (plus optionally "
                        "the '__common__' key for infos concerning all/no agents): "
                        "{} vs {}".format(infos.keys(), obs.keys())
                    )
            if "__all__" not in terminateds:
                raise ValueError(
                    "In multi-agent environments, '__all__': True|False must "
                    "be included in the 'terminateds' dict: got {}.".format(terminateds)
                )
            elif "__all__" not in truncateds:
                raise ValueError(
                    "In multi-agent environments, '__all__': True|False must "
                    "be included in the 'truncateds' dict: got {}.".format(truncateds)
                )

            if terminateds["__all__"]:
                self.terminateds.add(env_id)
            if truncateds["__all__"]:
                self.truncateds.add(env_id)
            self.env_states[env_id].observe(
                obs, rewards, terminateds, truncateds, infos
            )

    @override(BaseEnv)
    def try_reset(
        self,
        env_id: Optional[EnvID] = None,
        *,
        seed: Optional[int] = None,
        options: Optional[dict] = None,
    ) -> Optional[Tuple[MultiEnvDict, MultiEnvDict]]:
        ret_obs = {}
        ret_infos = {}
        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, infos = self.env_states[idx].reset(seed=seed, options=options)

            if isinstance(obs, Exception):
                if self.restart_failed_sub_environments:
                    self.env_states[idx].env = self.envs[idx] = self.make_env(idx)
                else:
                    raise obs
            else:
                assert isinstance(obs, dict), "Not a multi-agent obs dict!"
            if obs is not None:
                if idx in self.terminateds:
                    self.terminateds.remove(idx)
                if idx in self.truncateds:
                    self.truncateds.remove(idx)
            ret_obs[idx] = obs
            ret_infos[idx] = infos
        return ret_obs, ret_infos

    @override(BaseEnv)
    def try_restart(self, env_id: Optional[EnvID] = None) -> None:
        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:
            # Try closing down the old (possibly faulty) sub-env, but ignore errors.
            try:
                self.envs[idx].close()
            except Exception as e:
                if log_once("close_sub_env"):
                    logger.warning(
                        "Trying to close old and replaced sub-environment (at vector "
                        f"index={idx}), but closing resulted in error:\n{e}"
                    )
            # Try recreating the sub-env.
            logger.warning(f"Trying to restart sub-environment at index {idx}.")
            self.env_states[idx].env = self.envs[idx] = self.make_env(idx)
            logger.warning(f"Sub-environment at index {idx} restarted successfully.")

    @override(BaseEnv)
    def get_sub_environments(
        self, as_dict: bool = False
    ) -> Union[Dict[str, EnvType], List[EnvType]]:
        if as_dict:
            return {_id: env_state.env 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)
    def observation_space(self) -> gym.spaces.Dict:
        return self.envs[0].observation_space

    @property
    @override(BaseEnv)
    def action_space(self) -> gym.Space:
        return self.envs[0].action_space

    @override(BaseEnv)
    def get_agent_ids(self) -> Set[AgentID]:
        return self.envs[0].get_agent_ids()

    def _init_env_state(self, idx: Optional[int] = None) -> None:
        """Resets all or one particular sub-environment's state (by index).

        Args:
            idx: The index to reset at. If None, reset all the sub-environments' states.
        """
        # If index is None, reset all sub-envs' states:
        if idx is None:
            self.env_states = [
                _MultiAgentEnvState(env, self.restart_failed_sub_environments)
                for env in self.envs
            ]
        # Index provided, reset only the sub-env's state at the given index.
        else:
            assert isinstance(idx, int)
            self.env_states[idx] = _MultiAgentEnvState(
                self.envs[idx], self.restart_failed_sub_environments
            )


@OldAPIStack
class _MultiAgentEnvState:
    def __init__(self, env: MultiAgentEnv, return_error_as_obs: bool = False):
        assert isinstance(env, MultiAgentEnv)
        self.env = env
        self.return_error_as_obs = return_error_as_obs

        self.initialized = False
        self.last_obs = {}
        self.last_rewards = {}
        self.last_terminateds = {"__all__": False}
        self.last_truncateds = {"__all__": False}
        self.last_infos = {}

    def poll(
        self,
    ) -> Tuple[
        MultiAgentDict,
        MultiAgentDict,
        MultiAgentDict,
        MultiAgentDict,
        MultiAgentDict,
    ]:
        if not self.initialized:
            # TODO(sven): Should we make it possible to pass in a seed here?
            self.reset()
            self.initialized = True

        observations = self.last_obs
        rewards = {}
        terminateds = {"__all__": self.last_terminateds["__all__"]}
        truncateds = {"__all__": self.last_truncateds["__all__"]}
        infos = self.last_infos

        # If episode is done or we have an error, release everything we have.
        if (
            terminateds["__all__"]
            or truncateds["__all__"]
            or isinstance(observations, Exception)
        ):
            rewards = self.last_rewards
            self.last_rewards = {}
            terminateds = self.last_terminateds
            if isinstance(observations, Exception):
                terminateds["__all__"] = True
                truncateds["__all__"] = False
            self.last_terminateds = {}
            truncateds = self.last_truncateds
            self.last_truncateds = {}
            self.last_obs = {}
            infos = self.last_infos
            self.last_infos = {}
        # Only release those agents' rewards/terminateds/truncateds/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_terminateds:
                    terminateds[ag] = self.last_terminateds[ag]
                    del self.last_terminateds[ag]
                if ag in self.last_truncateds:
                    truncateds[ag] = self.last_truncateds[ag]
                    del self.last_truncateds[ag]

        self.last_terminateds["__all__"] = False
        self.last_truncateds["__all__"] = False
        return observations, rewards, terminateds, truncateds, infos

    def observe(
        self,
        obs: MultiAgentDict,
        rewards: MultiAgentDict,
        terminateds: MultiAgentDict,
        truncateds: 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 terminateds.items():
            if ag in self.last_terminateds:
                self.last_terminateds[ag] = self.last_terminateds[ag] or d
            else:
                self.last_terminateds[ag] = d
        for ag, t in truncateds.items():
            if ag in self.last_truncateds:
                self.last_truncateds[ag] = self.last_truncateds[ag] or t
            else:
                self.last_truncateds[ag] = t
        self.last_infos = infos

    def reset(
        self,
        *,
        seed: Optional[int] = None,
        options: Optional[dict] = None,
    ) -> Tuple[MultiAgentDict, MultiAgentDict]:
        try:
            obs_and_infos = self.env.reset(seed=seed, options=options)
        except Exception as e:
            if self.return_error_as_obs:
                logger.exception(e.args[0])
                obs_and_infos = e, e
            else:
                raise e

        self.last_obs, self.last_infos = obs_and_infos
        self.last_rewards = {}
        self.last_terminateds = {"__all__": False}
        self.last_truncateds = {"__all__": False}

        return self.last_obs, self.last_infos