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