text: from typing import Tuple, Dict, List
  import gym
  
  from ray.rllib.utils.annotations import PublicAPI
  from ray.rllib.utils.types import MultiAgentDict, AgentID
  
  # If the obs space is Dict type, look for the global state under this key.
  ENV_STATE = "state"
  
  
  @PublicAPI
  class MultiAgentEnv:
      """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 agents.
  
      Examples:
          >>> env = MyMultiAgentEnv()
          >>> obs = env.reset()
          >>> print(obs)
          {
              "car_0": [2.4, 1.6],
              "car_1": [3.4, -3.2],
              "traffic_light_1": [0, 3, 5, 1],
          }
          >>> obs, rewards, dones, infos = env.step(
          ...    action_dict={
          ...        "car_0": 1, "car_1": 0, "traffic_light_1": 2,
          ...    })
          >>> print(rewards)
          {
              "car_0": 3,
              "car_1": -1,
              "traffic_light_1": 0,
          }
          >>> print(dones)
          {
              "car_0": False,    # car_0 is still running
              "car_1": True,     # car_1 is done
              "__all__": False,  # the env is not done
          }
          >>> print(infos)
          {
              "car_0": {},  # info for car_0
              "car_1": {},  # info for car_1
          }
      """
  
      @PublicAPI
      def reset(self) -> MultiAgentDict:
          """Resets the env and returns observations from ready agents.
  
          Returns:
              obs (dict): New observations for each ready agent.
          """
          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
          -------
              obs (dict): New observations for each ready agent.
              rewards (dict): Reward values for each ready agent. If the
                  episode is just started, the value will be None.
              dones (dict): Done values for each ready agent. The special key
                  "__all__" (required) is used to indicate env termination.
              infos (dict): Optional info values for each agent id.
          """
          raise NotImplementedError
  
  # yapf: disable
  # __grouping_doc_begin__
      @PublicAPI
      def with_agent_groups(
              self,
              groups: Dict[str, List[AgentID]],
              obs_space: gym.Space = None,
              act_space: gym.Space = None) -> "MultiAgentEnv":
          """Convenience method for grouping together agents in this env.
  
          An agent group is a list of agent ids that are mapped to a single
          logical agent. All agents of the group must act at the same time in the
          environment. The grouped agent exposes Tuple action and observation
          spaces that are the concatenated action and obs spaces of the
          individual agents.
  
          The rewards of all the agents in a group are summed. The individual
          agent rewards are available under the "individual_rewards" key of the
          group info return.
  
          Agent grouping is required to leverage algorithms such as Q-Mix.
  
          This API is experimental.
  
          Arguments:
              groups (dict): Mapping from group id to a list of the agent ids
                  of group members. If an agent id is not present in any group
                  value, it will be left ungrouped.
              obs_space (Space): Optional observation space for the grouped
                  env. Must be a tuple space.
              act_space (Space): Optional action space for the grouped env.
                  Must be a tuple space.
  
          Examples:
              >>> env = YourMultiAgentEnv(...)
              >>> grouped_env = env.with_agent_groups(env, {
              ...   "group1": ["agent1", "agent2", "agent3"],
              ...   "group2": ["agent4", "agent5"],
              ... })
          """
  
          from ray.rllib.env.group_agents_wrapper import _GroupAgentsWrapper
          return _GroupAgentsWrapper(self, groups, obs_space, act_space)
  # __grouping_doc_end__
  # yapf: enable
  