b86d78766b
- fix broken links and out-of-the-date content - add pydocstyle and doc8 check - remove collector.seed and collector.render
64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
import gym
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import numpy as np
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from typing import Tuple
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from abc import ABC, abstractmethod
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class MultiAgentEnv(ABC, gym.Env):
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"""The interface for multi-agent environments.
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Multi-agent environments must be wrapped as
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:class:`~tianshou.env.MultiAgentEnv`. Here is the usage:
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::
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env = MultiAgentEnv(...)
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# obs is a dict containing obs, agent_id, and mask
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obs = env.reset()
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action = policy(obs)
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obs, rew, done, info = env.step(action)
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env.close()
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The available action's mask is set to 1, otherwise it is set to 0. Further
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usage can be found at :ref:`marl_example`.
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"""
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def __init__(self, **kwargs) -> None:
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pass
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@abstractmethod
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def reset(self) -> dict:
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"""Reset the state.
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Return the initial state, first agent_id, and the initial action set,
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for example, ``{'obs': obs, 'agent_id': agent_id, 'mask': mask}``
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"""
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pass
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@abstractmethod
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def step(self, action: np.ndarray
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) -> Tuple[dict, np.ndarray, np.ndarray, np.ndarray]:
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"""Run one timestep of the environment’s dynamics.
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When the end of episode is reached, you are responsible for calling
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reset() to reset the environment’s state.
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Accept action and return a tuple (obs, rew, done, info).
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:param numpy.ndarray action: action provided by a agent.
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:return: A tuple including four items:
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* ``obs`` a dict containing obs, agent_id, and mask, which means \
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that it is the ``agent_id`` player's turn to play with ``obs``\
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observation and ``mask``.
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* ``rew`` a numpy.ndarray, the amount of rewards returned after \
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previous actions. Depending on the specific environment, this \
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can be either a scalar reward for current agent or a vector \
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reward for all the agents.
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* ``done`` a numpy.ndarray, whether the episode has ended, in \
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which case further step() calls will return undefined results
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* ``info`` a numpy.ndarray, contains auxiliary diagnostic \
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information (helpful for debugging, and sometimes learning)
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"""
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pass
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