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| from typing import Dict | |
| import gym | |
| import numpy as np | |
| from ding.envs import ObsNormWrapper, RewardNormWrapper, DelayRewardWrapper, EvalEpisodeReturnWrapper | |
| def wrap_mujoco( | |
| env_id, | |
| norm_obs: Dict = dict(use_norm=False, ), | |
| norm_reward: Dict = dict(use_norm=False, ), | |
| delay_reward_step: int = 1 | |
| ) -> gym.Env: | |
| r""" | |
| Overview: | |
| Wrap Mujoco 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 | |
| """ | |
| # import customized gym environment | |
| from . import mujoco_gym_env | |
| env = gym.make(env_id) | |
| env = EvalEpisodeReturnWrapper(env) | |
| 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) | |
| if delay_reward_step > 1: | |
| env = DelayRewardWrapper(env, delay_reward_step) | |
| return env | |