mindspore_rl.environment.sc2_environment 源代码

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"""
The starcraft 2 environment.
"""

# pylint: disable=C0111
import importlib

import numpy as np

from mindspore_rl.environment.python_environment import PythonEnvironment
from mindspore_rl.environment.space import Space


[文档]class StarCraft2Environment(PythonEnvironment): """ StarCraft2Environment is a wrapper of SMAC. SMAC is WhiRL's environment for research in the field of collaborative multi-agent reinforcement learning (MARL) based on Blizzard's StarCraft II RTS game. SMAC makes use of Blizzard's StarCraft II Machine Learning API and DeepMind's PySC2 to provide a convenient interface for autonomous agents to interact with StarCraft II, getting observations and performing actions. More detail please have a look at the official github of SMAC: https://github.com/oxwhirl/smac. Args: params (dict): A dictionary contains all the parameters which are used in this class. +------------------------------+--------------------------------------------------------+ | Configuration Parameters | Notices | +==============================+========================================================+ | sc2_args | a dict which contains key value that is used to create| | | instance of SMAC, such as map_name. For more detail | | | please have a look at its official github. | +------------------------------+--------------------------------------------------------+ env_id (int, optional): A integer which is used to set the seed of this environment, default value means the 0th environment. Default: ``0`` . Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> env_params = {'sc2_args': {'map_name': '2s3z'}} >>> environment = StarCraft2Environment(env_params, 0) >>> print(environment) """ def __init__(self, params, env_id=0): sc2_args = params["sc2_args"] if sc2_args.get("seed"): sc2_args["seed"] = sc2_args["seed"] + env_id * 1000 sc2_creator = importlib.import_module("smac.env") self._env = sc2_creator.StarCraft2Env(**sc2_args) self._env.reset() valid_action_mask = self._env.get_avail_actions() self._info_key = params.get("info_key", None) env_info = self._env.get_env_info() num_agent = env_info["n_agents"] action_dim = env_info["n_actions"] obs_dim = env_info["obs_shape"] config = { **env_info, "global_observation_dim": env_info["state_shape"], "num_agent": num_agent, } self.step_info = {} observation_space = Space((obs_dim,), np.float32, batch_shape=(num_agent,)) action_space = Space( (1,), np.int32, low=0, high=action_dim, batch_shape=(num_agent,), mask=valid_action_mask, ) super().__init__(action_space, observation_space, config=config)
[文档] def close(self): r""" Close the environment to release the resource. Returns: Success(np.bool\_), Whether shutdown the process or threading successfully. """ self._env.close() return True
def _step(self, action): reward, done, step_info = self._env.step(action) info_out = [] if self._info_key is not None: for key in self._info_key: info_out.append(np.array(step_info[key])) new_state = np.array(self._env.get_obs(), self.observation_space.np_dtype) reward = np.array(reward, self.reward_space.np_dtype) done = np.array(done) global_obs = self._env.get_state() avail_actions = np.array( self._env.get_avail_actions(), self._action_space.np_dtype ) if len(info_out) > 0: step_out = (new_state, reward, done, global_obs, avail_actions, *info_out) else: step_out = (new_state, reward, done, global_obs, avail_actions) return step_out def _reset(self): local_obs, global_obs = self._env.reset() avail_actions = np.array( self._env.get_avail_actions(), self._action_space.np_dtype ) return np.array(local_obs), np.array(global_obs), avail_actions def _render(self) -> np.ndarray: img = self._env.render() return img def _set_seed(self, seed_value: int) -> bool: """Inner set seed""" raise ValueError( "StarCraft2Environment does not support set seed. Please pass seed through params" )