hunk.code_window:                 "MultiAgentEnv, VectorEnv, RemoteBaseEnv, ExternalMultiAgentEnv, "
                   f"ExternalEnv, but instead was a {type(env)}"
               )
           if isinstance(env, MultiAgentEnv):
               check_multiagent_environments(env)
           elif isinstance(env, gym.Env):
               check_gym_environments(env)
  
hunk.labels: keep replace keep keep keep keep keep
hunk.after_edit:                 f"ExternalEnv, but instead is of type {type(env)}."
  
hunk.file_path: rllib/utils/pre_checks/env.py
hunk.type: replace
file: import 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.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__
      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).
  
          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
  
  
  @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.
  
      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
  
  
  @PublicAPI
  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._init_env_state(idx=None)
          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 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:
              # Recreate the sub-env.
              self.envs[idx] = self.make_env(idx)
              # Replace the multi-agent env state at the index.
              self._init_env_state(idx)
              # Remove done flag at index.
              if idx in self.dones:
                  self.dones.remove(idx)
  
      @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)
      @PublicAPI
      def observation_space(self) -> gym.spaces.Dict:
          return 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()
  
      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) 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])
  
  
  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
  