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