rllib/env/multi_agent_env.py
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
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
            }
        
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 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 .
def step(
        self, action_dict: MultiAgentDict
    ) -> Tuple[MultiAgentDict, MultiAgentDict, MultiAgentDict, MultiAgentDict]:
        raise NotImplementedError
def step ( self , action_dict : MultiAgentDict ) -> Tuple [ MultiAgentDict , MultiAgentDict , MultiAgentDict , MultiAgentDict ] : raise NotImplementedError
Returns observations from ready agents.
Returns observations from ready agents .
"""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
            }
        """
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.
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 .
self
action_dict
MultiAgentDict
examples
None