Source code for ray.rllib.env.multi_agent_env

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

from ray.rllib.env.base_env import BaseEnv
from ray.rllib.env.env_context import EnvContext
from ray.rllib.utils.annotations import (
    OldAPIStack,
    override,
    PublicAPI,
)
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__)


[docs]@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 Algorithms, which are also sometimes referred to as "agents" or "RL agents". The preferred format for action- and observation space is a mapping from agent ids to their individual spaces. If that is not provided, the respective methods' observation_space_contains(), action_space_contains(), action_space_sample() and observation_space_sample() have to be overwritten. """
[docs] def __init__(self): # TODO (sven): super init call seems to have been missing. Since forever. # super().__init__() 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, "_action_space_in_preferred_format"): self._action_space_in_preferred_format = None if not hasattr(self, "_obs_space_in_preferred_format"): self._obs_space_in_preferred_format = None
[docs] def reset( self, *, seed: Optional[int] = None, options: Optional[dict] = None, ) -> Tuple[MultiAgentDict, MultiAgentDict]: """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)
[docs] 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 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, "_obs_space_in_preferred_format") or self._obs_space_in_preferred_format is None ): self._obs_space_in_preferred_format = ( self._check_if_obs_space_maps_agent_id_to_sub_space() ) if self._obs_space_in_preferred_format: for key, agent_obs in x.items(): if not self.observation_space[key].contains(agent_obs): return False if not all(k in self.observation_space.spaces for k in x): if log_once("possibly_bad_multi_agent_dict_missing_agent_observations"): logger.warning( "You environment returns observations that are " "MultiAgentDicts with incomplete information. " "Meaning that they only contain information on a subset of" " participating agents. Ignore this warning if this is " "intended, for example if your environment is a turn-based " "simulation." ) return True logger.warning( "observation_space_contains() of {} has not been implemented. " "You " "can either implement it yourself or bring the observation " "space into the preferred format of a mapping from agent ids " "to their individual observation spaces. ".format(self) ) return True 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, "_action_space_in_preferred_format") or self._action_space_in_preferred_format is None ): self._action_space_in_preferred_format = ( self._check_if_action_space_maps_agent_id_to_sub_space() ) if self._action_space_in_preferred_format: return all(self.action_space[agent].contains(x[agent]) for agent in x) if log_once("action_space_contains"): logger.warning( "action_space_contains() of {} has not been implemented. " "You " "can either implement it yourself or bring the observation " "space into the preferred format of a mapping from agent ids " "to their individual observation spaces. ".format(self) ) return True 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, "_action_space_in_preferred_format") or self._action_space_in_preferred_format is None ): self._action_space_in_preferred_format = ( self._check_if_action_space_maps_agent_id_to_sub_space() ) if self._action_space_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( f"action_space_sample() of {self} has not been implemented. " "You can either implement it yourself or bring the observation " "space into the preferred format of a mapping from agent ids " "to their individual observation spaces." ) return {} 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, "_obs_space_in_preferred_format") or self._obs_space_in_preferred_format is None ): self._obs_space_in_preferred_format = ( self._check_if_obs_space_maps_agent_id_to_sub_space() ) if self._obs_space_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() of {} has not been implemented. " "You " "can either implement it yourself or bring the observation " "space into the preferred format of a mapping from agent ids " "to their individual observation spaces. ".format(self) ) return {} 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
[docs] def render(self) -> None: """Tries to render the environment.""" # By default, do nothing. pass
# fmt: off # __grouping_doc_begin__
[docs] 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 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 def _check_if_obs_space_maps_agent_id_to_sub_space(self) -> bool: """Checks if obs space maps from agent ids to spaces of individual agents.""" return ( hasattr(self, "observation_space") and isinstance(self.observation_space, gym.spaces.Dict) and set(self.observation_space.spaces.keys()) == self.get_agent_ids() ) def _check_if_action_space_maps_agent_id_to_sub_space(self) -> bool: """Checks if action space maps from agent ids to spaces of individual agents.""" return ( hasattr(self, "action_space") and isinstance(self.action_space, gym.spaces.Dict) and set(self.action_space.keys()) == self.get_agent_ids() )
[docs]@PublicAPI 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.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) .. testoutput:: {0: [...], 1: [...]} {0: [...], 1: [...], 2: [...]} """ class MultiEnv(MultiAgentEnv): def __init__(self, config: EnvContext = None): MultiAgentEnv.__init__(self) # Note(jungong) : explicitly check for None here, because config # can have an empty dict but meaningful data fields (worker_index, # vector_index) etc. # TODO(jungong) : 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_space = gym.spaces.Dict( {i: self.envs[i].observation_space for i in range(num)} ) self._obs_space_in_preferred_format = True self.action_space = gym.spaces.Dict( {i: self.envs[i].action_space for i in range(num)} ) self._action_space_in_preferred_format = True self._agent_ids = set(range(num)) @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 action for at least one agent if len(action_dict) == 0: raise ValueError( "The environment is expecting 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): return self.envs[0].render(self.render_mode) 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 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() 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