"""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.

"""

def __init__(self):

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

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)

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

def render(self) -> None:

"""Tries to render the environment."""

# By default, do nothing.

pass

# 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

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()

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):

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 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)

"""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

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}