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from typing import Optional, Callable
import gym
from gym.spaces import Box
import numpy as np
from ding.envs import BaseEnv, BaseEnvTimestep
from ding.envs.common.common_function import affine_transform
from ding.torch_utils import to_ndarray
from ding.utils import ENV_REGISTRY
import dmc2gym
from ding.envs import WarpFrameWrapper, ScaledFloatFrameWrapper, ClipRewardWrapper, ActionRepeatWrapper, FrameStackWrapper
def dmc2gym_observation_space(dim, minimum=-np.inf, maximum=np.inf, dtype=np.float32) -> Callable:
def observation_space(from_pixels=True, height=84, width=84, channels_first=True) -> Box:
if from_pixels:
shape = [3, height, width] if channels_first else [height, width, 3]
return Box(low=0, high=255, shape=shape, dtype=np.uint8)
else:
return Box(np.repeat(minimum, dim).astype(dtype), np.repeat(maximum, dim).astype(dtype), dtype=dtype)
return observation_space
def dmc2gym_state_space(dim, minimum=-np.inf, maximum=np.inf, dtype=np.float32) -> Box:
return Box(np.repeat(minimum, dim).astype(dtype), np.repeat(maximum, dim).astype(dtype), dtype=dtype)
def dmc2gym_action_space(dim, minimum=-1, maximum=1, dtype=np.float32) -> Box:
return Box(np.repeat(minimum, dim).astype(dtype), np.repeat(maximum, dim).astype(dtype), dtype=dtype)
def dmc2gym_reward_space(minimum=0, maximum=1, dtype=np.float32) -> Callable:
def reward_space(frame_skip=1) -> Box:
return Box(
np.repeat(minimum * frame_skip, 1).astype(dtype),
np.repeat(maximum * frame_skip, 1).astype(dtype),
dtype=dtype
)
return reward_space
"""
default observation, state, action, reward space for dmc2gym env
"""
dmc2gym_env_info = {
"ball_in_cup": {
"catch": {
"observation_space": dmc2gym_observation_space(8),
"state_space": dmc2gym_state_space(8),
"action_space": dmc2gym_action_space(2),
"reward_space": dmc2gym_reward_space()
}
},
"cartpole": {
"balance": {
"observation_space": dmc2gym_observation_space(5),
"state_space": dmc2gym_state_space(5),
"action_space": dmc2gym_action_space(1),
"reward_space": dmc2gym_reward_space()
},
"swingup": {
"observation_space": dmc2gym_observation_space(5),
"state_space": dmc2gym_state_space(5),
"action_space": dmc2gym_action_space(1),
"reward_space": dmc2gym_reward_space()
}
},
"cheetah": {
"run": {
"observation_space": dmc2gym_observation_space(17),
"state_space": dmc2gym_state_space(17),
"action_space": dmc2gym_action_space(6),
"reward_space": dmc2gym_reward_space()
}
},
"finger": {
"spin": {
"observation_space": dmc2gym_observation_space(9),
"state_space": dmc2gym_state_space(9),
"action_space": dmc2gym_action_space(1),
"reward_space": dmc2gym_reward_space()
}
},
"reacher": {
"easy": {
"observation_space": dmc2gym_observation_space(6),
"state_space": dmc2gym_state_space(6),
"action_space": dmc2gym_action_space(2),
"reward_space": dmc2gym_reward_space()
}
},
"walker": {
"walk": {
"observation_space": dmc2gym_observation_space(24),
"state_space": dmc2gym_state_space(24),
"action_space": dmc2gym_action_space(6),
"reward_space": dmc2gym_reward_space()
}
}
}
@ENV_REGISTRY.register('dmc2gym')
class DMC2GymEnv(BaseEnv):
def __init__(self, cfg: dict = {}) -> None:
assert cfg.domain_name in dmc2gym_env_info, '{}/{}'.format(cfg.domain_name, dmc2gym_env_info.keys())
assert cfg.task_name in dmc2gym_env_info[
cfg.domain_name], '{}/{}'.format(cfg.task_name, dmc2gym_env_info[cfg.domain_name].keys())
# default config for dmc2gym env
self._cfg = {
"frame_skip": 4,
'warp_frame': False,
'scale': False,
'clip_rewards': False,
'action_repeat': 1,
"frame_stack": 3,
"from_pixels": True,
"visualize_reward": False,
"height": 84,
"width": 84,
"channels_first": True,
"resize": 84,
}
self._cfg.update(cfg)
self._init_flag = False
self._replay_path = None
self._observation_space = dmc2gym_env_info[cfg.domain_name][cfg.task_name]["observation_space"](
from_pixels=self._cfg["from_pixels"],
height=self._cfg["height"],
width=self._cfg["width"],
channels_first=self._cfg["channels_first"]
)
self._action_space = dmc2gym_env_info[cfg.domain_name][cfg.task_name]["action_space"]
self._reward_space = dmc2gym_env_info[cfg.domain_name][cfg.task_name]["reward_space"](self._cfg["frame_skip"])
def reset(self) -> np.ndarray:
if not self._init_flag:
self._env = dmc2gym.make(
domain_name=self._cfg["domain_name"],
task_name=self._cfg["task_name"],
seed=1,
visualize_reward=self._cfg["visualize_reward"],
from_pixels=self._cfg["from_pixels"],
height=self._cfg["height"],
width=self._cfg["width"],
frame_skip=self._cfg["frame_skip"],
channels_first=self._cfg["channels_first"],
)
# optional env wrapper
if self._cfg['warp_frame']:
self._env = WarpFrameWrapper(self._env, size=self._cfg['resize'])
if self._cfg['scale']:
self._env = ScaledFloatFrameWrapper(self._env)
if self._cfg['clip_rewards']:
self._env = ClipRewardWrapper(self._env)
if self._cfg['action_repeat']:
self._env = ActionRepeatWrapper(self._env, self._cfg['action_repeat'])
if self._cfg['frame_stack'] > 1:
self._env = FrameStackWrapper(self._env, self._cfg['frame_stack'])
# set the obs, action space of wrapped env
self._observation_space = self._env.observation_space
self._action_space = self._env.action_space
if self._replay_path is not None:
if gym.version.VERSION > '0.22.0':
self._env.metadata.update({'render_modes': ["rgb_array"]})
else:
self._env.metadata.update({'render.modes': ["rgb_array"]})
self._env = gym.wrappers.RecordVideo(
self._env,
video_folder=self._replay_path,
episode_trigger=lambda episode_id: True,
name_prefix='rl-video-{}'.format(id(self))
)
self._env.start_video_recorder()
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)
self._eval_episode_return = 0
obs = self._env.reset()
obs = to_ndarray(obs).astype(np.float32)
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: np.ndarray) -> BaseEnvTimestep:
action = action.astype('float32')
action = affine_transform(action, min_val=self._env.action_space.low, max_val=self._env.action_space.high)
obs, rew, done, info = self._env.step(action)
self._eval_episode_return += rew
if done:
info['eval_episode_return'] = self._eval_episode_return
obs = to_ndarray(obs).astype(np.float32)
rew = to_ndarray([rew]).astype(np.float32) # wrapped to be transferred to a array with shape (1,)
return BaseEnvTimestep(obs, rew, done, info)
def enable_save_replay(self, replay_path: Optional[str] = None) -> None:
if replay_path is None:
replay_path = './video'
self._replay_path = replay_path
def random_action(self) -> np.ndarray:
random_action = self.action_space.sample().astype(np.float32)
return random_action
@property
def observation_space(self) -> gym.spaces.Space:
return self._observation_space
@property
def action_space(self) -> gym.spaces.Space:
return self._action_space
@property
def reward_space(self) -> gym.spaces.Space:
return self._reward_space
def __repr__(self) -> str:
return "DI-engine DeepMind Control Suite to gym Env: " + self._cfg["domain_name"] + ":" + self._cfg["task_name"]
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