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| from typing import Any, List, Union, Sequence | |
| import copy | |
| import gym | |
| import numpy as np | |
| from ding.envs import BaseEnv, BaseEnvTimestep | |
| from ding.utils import ENV_REGISTRY | |
| from ding.torch_utils import to_ndarray, to_list | |
| from .atari_wrappers import wrap_deepmind | |
| from pprint import pprint | |
| def PomdpEnv(cfg, only_info=False): | |
| ''' | |
| For debug purpose, create an env follow openai gym standard so it can be widely test by | |
| other library with same environment setting in DI-engine | |
| env = PomdpEnv(cfg) | |
| obs = env.reset() | |
| obs, reward, done, info = env.step(action) | |
| ''' | |
| env = wrap_deepmind( | |
| cfg.env_id, | |
| frame_stack=cfg.frame_stack, | |
| episode_life=cfg.is_train, | |
| clip_rewards=cfg.is_train, | |
| warp_frame=cfg.warp_frame, | |
| use_ram=cfg.use_ram, | |
| render=cfg.render, | |
| pomdp=cfg.pomdp, | |
| only_info=only_info, | |
| ) | |
| return env | |
| class PomdpAtariEnv(BaseEnv): | |
| def __init__(self, cfg: dict) -> None: | |
| self._cfg = cfg | |
| self._init_flag = False | |
| def reset(self) -> Sequence: | |
| if not self._init_flag: | |
| self._env = self._make_env(only_info=False) | |
| self._init_flag = True | |
| self._observation_space = self._env.observation_space | |
| self._action_space = self._env.action_space | |
| self._reward_space = gym.spaces.Box( | |
| low=self._env.reward_range[0], high=self._env.reward_range[1], shape=(1, ), dtype=np.float32 | |
| ) | |
| 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) | |
| 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: np.ndarray) -> BaseEnvTimestep: | |
| assert isinstance(action, np.ndarray), type(action) | |
| action = action.item() | |
| obs, rew, done, info = self._env.step(action) | |
| self._eval_episode_return += rew | |
| obs = to_ndarray(obs) | |
| 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 _make_env(self, only_info=False): | |
| return wrap_deepmind( | |
| self._cfg.env_id, | |
| episode_life=self._cfg.is_train, | |
| clip_rewards=self._cfg.is_train, | |
| pomdp=self._cfg.pomdp, | |
| frame_stack=self._cfg.frame_stack, | |
| warp_frame=self._cfg.warp_frame, | |
| use_ram=self._cfg.use_ram, | |
| only_info=only_info, | |
| ) | |
| def __repr__(self) -> str: | |
| return "DI-engine POMDP Atari Env({})".format(self._cfg.env_id) | |
| def create_collector_env_cfg(cfg: dict) -> List[dict]: | |
| collector_env_num = cfg.pop('collector_env_num', 1) | |
| cfg = copy.deepcopy(cfg) | |
| cfg.is_train = True | |
| return [cfg for _ in range(collector_env_num)] | |
| def create_evaluator_env_cfg(cfg: dict) -> List[dict]: | |
| evaluator_env_num = cfg.pop('evaluator_env_num', 1) | |
| cfg = copy.deepcopy(cfg) | |
| cfg.is_train = False | |
| return [cfg for _ in range(evaluator_env_num)] | |
| def observation_space(self) -> gym.spaces.Space: | |
| return self._observation_space | |
| def action_space(self) -> gym.spaces.Space: | |
| return self._action_space | |
| def reward_space(self) -> gym.spaces.Space: | |
| return self._reward_space | |