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from typing import Any, Union, List |
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import copy |
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import numpy as np |
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from ditk import logging |
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from ding.envs import BaseEnv, BaseEnvTimestep, BaseEnvInfo, update_shape |
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from ding.envs.common.env_element import EnvElement, EnvElementInfo |
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from ding.envs.common.common_function import affine_transform |
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from ding.torch_utils import to_ndarray, to_list |
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from ding.utils import ENV_REGISTRY |
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from .pybullet_wrappers import wrap_pybullet |
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Pybullet_INFO_DICT = { |
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'InvertedPendulumMuJoCoEnv-v0': BaseEnvInfo( |
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agent_num=1, |
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obs_space=EnvElementInfo( |
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shape=(4, ), |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf"), |
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'dtype': np.float32 |
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}, |
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), |
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act_space=EnvElementInfo( |
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shape=(1, ), |
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value={ |
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'min': -1.0, |
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'max': 1.0, |
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'dtype': np.float32 |
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}, |
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), |
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rew_space=EnvElementInfo( |
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shape=1, |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf") |
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}, |
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), |
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use_wrappers=None, |
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), |
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'InvertedDoublePendulumMuJoCoEnv-v0': BaseEnvInfo( |
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agent_num=1, |
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obs_space=EnvElementInfo( |
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shape=(11, ), |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf"), |
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'dtype': np.float32 |
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}, |
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), |
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act_space=EnvElementInfo( |
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shape=(1, ), |
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value={ |
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'min': -1.0, |
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'max': 1.0, |
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'dtype': np.float32 |
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}, |
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), |
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rew_space=EnvElementInfo( |
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shape=1, |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf") |
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}, |
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), |
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use_wrappers=None, |
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), |
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'Walker2DMuJoCoEnv-v0': BaseEnvInfo( |
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agent_num=1, |
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obs_space=EnvElementInfo( |
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shape=(17, ), |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf"), |
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'dtype': np.float32 |
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}, |
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), |
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act_space=EnvElementInfo( |
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shape=(6, ), |
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value={ |
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'min': -1.0, |
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'max': 1.0, |
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'dtype': np.float32 |
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}, |
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), |
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rew_space=EnvElementInfo( |
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shape=1, |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf") |
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}, |
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), |
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use_wrappers=None, |
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), |
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'Walker2DPyBulletEnv-v0': BaseEnvInfo( |
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agent_num=1, |
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obs_space=EnvElementInfo( |
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shape=(22, ), |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf"), |
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'dtype': np.float32 |
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}, |
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), |
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act_space=EnvElementInfo( |
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shape=(6, ), |
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value={ |
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'min': -1.0, |
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'max': 1.0, |
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'dtype': np.float32 |
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}, |
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), |
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rew_space=EnvElementInfo( |
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shape=1, |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf") |
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}, |
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), |
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use_wrappers=None, |
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), |
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'HalfCheetahMuJoCoEnv-v0': BaseEnvInfo( |
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agent_num=1, |
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obs_space=EnvElementInfo( |
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shape=(17, ), |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf"), |
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'dtype': np.float32 |
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}, |
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), |
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act_space=EnvElementInfo( |
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shape=(6, ), |
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value={ |
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'min': -1.0, |
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'max': 1.0, |
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'dtype': np.float32 |
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}, |
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), |
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rew_space=EnvElementInfo( |
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shape=1, |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf") |
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}, |
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), |
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use_wrappers=None, |
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), |
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'HalfCheetahPyBulletEnv-v0': BaseEnvInfo( |
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agent_num=1, |
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obs_space=EnvElementInfo( |
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shape=(26, ), |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf"), |
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'dtype': np.float32 |
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}, |
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), |
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act_space=EnvElementInfo( |
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shape=(6, ), |
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value={ |
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'min': -1.0, |
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'max': 1.0, |
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'dtype': np.float32 |
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}, |
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), |
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rew_space=EnvElementInfo( |
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shape=1, |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf") |
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}, |
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), |
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use_wrappers=None, |
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), |
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'AntMuJoCoEnv-v0': BaseEnvInfo( |
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agent_num=1, |
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obs_space=EnvElementInfo( |
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shape=(111, ), |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf"), |
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'dtype': np.float32 |
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}, |
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), |
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act_space=EnvElementInfo( |
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shape=(8, ), |
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value={ |
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'min': -1.0, |
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'max': 1.0, |
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'dtype': np.float32 |
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}, |
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), |
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rew_space=EnvElementInfo( |
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shape=1, |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf") |
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}, |
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), |
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use_wrappers=None, |
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), |
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'AntPyBulletEnv-v0': BaseEnvInfo( |
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agent_num=1, |
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obs_space=EnvElementInfo( |
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shape=(28, ), |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf"), |
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'dtype': np.float32 |
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}, |
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), |
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act_space=EnvElementInfo( |
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shape=(8, ), |
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value={ |
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'min': -1.0, |
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'max': 1.0, |
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'dtype': np.float32 |
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}, |
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), |
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rew_space=EnvElementInfo( |
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shape=1, |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf") |
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}, |
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), |
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use_wrappers=None, |
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), |
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'HopperMuJoCoEnv-v0': BaseEnvInfo( |
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agent_num=1, |
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obs_space=EnvElementInfo( |
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shape=(11, ), |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf"), |
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'dtype': np.float32 |
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}, |
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), |
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act_space=EnvElementInfo( |
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shape=(3, ), |
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value={ |
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'min': -1.0, |
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'max': 1.0, |
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'dtype': np.float32 |
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}, |
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), |
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rew_space=EnvElementInfo( |
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shape=1, |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf") |
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}, |
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), |
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use_wrappers=None, |
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), |
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'HopperPyBulletEnv-v0': BaseEnvInfo( |
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agent_num=1, |
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obs_space=EnvElementInfo( |
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shape=(15, ), |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf"), |
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'dtype': np.float32 |
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}, |
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), |
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act_space=EnvElementInfo( |
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shape=(3, ), |
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value={ |
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'min': -1.0, |
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'max': 1.0, |
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'dtype': np.float32 |
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}, |
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), |
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rew_space=EnvElementInfo( |
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shape=1, |
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value={ |
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'min': np.float64("-inf"), |
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'max': np.float64("inf") |
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}, |
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), |
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use_wrappers=None, |
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), |
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} |
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@ENV_REGISTRY.register('pybullet') |
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class PybulletEnv(BaseEnv): |
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""" |
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Note: |
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Due to the open source of mujoco env, DI-engine will deprecate PyBullet env. If anyone needs it, \ |
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please add a new issue and we will continue to maintain it. |
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""" |
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def __init__(self, cfg: dict) -> None: |
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logging.warning('PybulletEnv is deprecated, if anyone needs it, please add a new issue.') |
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self._cfg = cfg |
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self._use_act_scale = cfg.use_act_scale |
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self._init_flag = False |
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def reset(self) -> np.ndarray: |
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if not self._init_flag: |
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self._env = self._make_env(only_info=False) |
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self._init_flag = True |
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if hasattr(self, '_seed') and hasattr(self, '_dynamic_seed') and self._dynamic_seed: |
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np_seed = 100 * np.random.randint(1, 1000) |
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self._env.seed(self._seed + np_seed) |
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elif hasattr(self, '_seed'): |
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self._env.seed(self._seed) |
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obs = self._env.reset() |
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obs = to_ndarray(obs).astype('float32') |
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self._eval_episode_return = 0. |
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return obs |
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def close(self) -> None: |
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if self._init_flag: |
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self._env.close() |
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self._init_flag = False |
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def seed(self, seed: int, dynamic_seed: bool = True) -> None: |
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self._seed = seed |
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self._dynamic_seed = dynamic_seed |
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np.random.seed(self._seed) |
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def step(self, action: Union[np.ndarray, list]) -> BaseEnvTimestep: |
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action = to_ndarray(action) |
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if self._use_act_scale: |
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action_range = self.info().act_space.value |
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action = affine_transform(action, min_val=action_range['min'], max_val=action_range['max']) |
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obs, rew, done, info = self._env.step(action) |
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self._eval_episode_return += rew |
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obs = to_ndarray(obs).astype('float32') |
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rew = to_ndarray([rew]) |
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if done: |
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info['eval_episode_return'] = self._eval_episode_return |
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return BaseEnvTimestep(obs, rew, done, info) |
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def info(self) -> BaseEnvInfo: |
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if self._cfg.env_id in Pybullet_INFO_DICT: |
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info = copy.deepcopy(Pybullet_INFO_DICT[self._cfg.env_id]) |
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info.use_wrappers = self._make_env(only_info=True) |
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obs_shape, act_shape, rew_shape = update_shape( |
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info.obs_space.shape, info.act_space.shape, info.rew_space.shape, info.use_wrappers.split('\n') |
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) |
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info.obs_space.shape = obs_shape |
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info.act_space.shape = act_shape |
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info.rew_space.shape = rew_shape |
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return info |
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else: |
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keys = Pybullet_INFO_DICT.keys() |
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raise NotImplementedError('{} not found in Pybullet_INFO_DICT [{}]'.format(self._cfg.env_id, keys)) |
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def _make_env(self, only_info=False): |
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return wrap_pybullet( |
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self._cfg.env_id, |
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norm_obs=self._cfg.get('norm_obs', None), |
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norm_reward=self._cfg.get('norm_reward', None), |
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only_info=only_info |
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) |
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def __repr__(self) -> str: |
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return "DI-engine Pybullet Env({})".format(self._cfg.env_id) |
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@staticmethod |
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def create_collector_env_cfg(cfg: dict) -> List[dict]: |
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collector_cfg = copy.deepcopy(cfg) |
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collector_env_num = collector_cfg.pop('collector_env_num', 1) |
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return [collector_cfg for _ in range(collector_env_num)] |
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@staticmethod |
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def create_evaluator_env_cfg(cfg: dict) -> List[dict]: |
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evaluator_cfg = copy.deepcopy(cfg) |
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evaluator_env_num = evaluator_cfg.pop('evaluator_env_num', 1) |
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evaluator_cfg.norm_reward.use_norm = False |
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return [evaluator_cfg for _ in range(evaluator_env_num)] |
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