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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
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