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| import gym | |
| import numpy as np | |
| from ditk import logging | |
| from ding.envs import ObsNormWrapper, RewardNormWrapper | |
| try: | |
| import pybulletgym # register PyBullet enviroments with open ai gym | |
| except ImportError: | |
| logging.warning("not found pybullet env, please install it, refer to https://github.com/benelot/pybullet-gym") | |
| def wrap_pybullet(env_id, norm_obs=True, norm_reward=True, only_info=False) -> gym.Env: | |
| r""" | |
| Overview: | |
| Wrap Pybullet Env to preprocess env step's return info, e.g. observation normalization, reward normalization, etc. | |
| Arguments: | |
| - env_id (:obj:`str`): Pybullet 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 Pybullet 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 | |