| from typing import Dict | |
| import gym | |
| from ditk import logging | |
| from ding.envs import ObsNormWrapper, RewardNormWrapper | |
| try: | |
| import gym_sokoban | |
| except ImportError: | |
| logging.warning("not found sokoban env, please install it, refer to https://github.com/mpSchrader/gym-sokoban") | |
| def wrap_sokoban( | |
| env_id, norm_obs: bool = False, norm_reward: Dict = dict(use_norm=False, ), only_info=False | |
| ) -> gym.Env: | |
| r""" | |
| Overview: | |
| Wrap Sokoban Env to preprocess env step's return info, e.g. observation normalization, reward normalization, etc. | |
| Arguments: | |
| - env_id (:obj:`str`): Mujoco environment id, for example "HalfCheetah-v3" | |
| - norm_obs (:obj:`EasyDict`): Whether to normalize observation or not | |
| - norm_reward (:obj:`EasyDict`): Whether to normalize reward or not. For evaluator, environment's reward \ | |
| should not be normalized: Either ``norm_reward`` is None or ``norm_reward.use_norm`` is False can do this. | |
| Returns: | |
| - wrapped_env (:obj:`gym.Env`): The wrapped mujoco environment | |
| """ | |
| if not only_info: | |
| env = gym.make(env_id) | |
| if norm_obs is not None and norm_obs.use_norm: | |
| env = ObsNormWrapper(env) | |
| if norm_reward is not None and norm_reward.use_norm: | |
| env = RewardNormWrapper(env, norm_reward.reward_discount) | |
| return env | |
| else: | |
| wrapper_info = '' | |
| if norm_obs is not None and norm_obs.use_norm: | |
| wrapper_info = ObsNormWrapper.__name__ + '\n' | |
| if norm_reward is not None and norm_reward.use_norm: | |
| wrapper_info += RewardNormWrapper.__name__ + '\n' | |
| return wrapper_info | |