ray/python/ray/rllib/env/multi_agent_env.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


class MultiAgentEnv(object):
    """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_1": True,
            "__all__": False,
        }
    """

    def reset(self):
        """Resets the env and returns observations from ready agents.

        Returns:
            obs (dict): New observations for each ready agent.
        """
        raise NotImplementedError

    def step(self, action_dict):
        """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__" is used to indicate env termination.
            infos (dict): Info values for each ready agent.
        """
        raise NotImplementedError