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from typing import Any, List, Union, Sequence |
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import copy |
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import gym |
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import numpy as np |
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from ding.envs import BaseEnv, BaseEnvTimestep |
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from ding.utils import ENV_REGISTRY |
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from ding.torch_utils import to_ndarray, to_list |
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from .atari_wrappers import wrap_deepmind |
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from pprint import pprint |
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def PomdpEnv(cfg, only_info=False): |
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''' |
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For debug purpose, create an env follow openai gym standard so it can be widely test by |
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other library with same environment setting in DI-engine |
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env = PomdpEnv(cfg) |
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obs = env.reset() |
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obs, reward, done, info = env.step(action) |
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''' |
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env = wrap_deepmind( |
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cfg.env_id, |
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frame_stack=cfg.frame_stack, |
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episode_life=cfg.is_train, |
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clip_rewards=cfg.is_train, |
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warp_frame=cfg.warp_frame, |
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use_ram=cfg.use_ram, |
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render=cfg.render, |
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pomdp=cfg.pomdp, |
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only_info=only_info, |
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) |
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return env |
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@ENV_REGISTRY.register('pomdp') |
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class PomdpAtariEnv(BaseEnv): |
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def __init__(self, cfg: dict) -> None: |
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self._cfg = cfg |
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self._init_flag = False |
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def reset(self) -> Sequence: |
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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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self._observation_space = self._env.observation_space |
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self._action_space = self._env.action_space |
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self._reward_space = gym.spaces.Box( |
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low=self._env.reward_range[0], high=self._env.reward_range[1], shape=(1, ), dtype=np.float32 |
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) |
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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) |
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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: np.ndarray) -> BaseEnvTimestep: |
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assert isinstance(action, np.ndarray), type(action) |
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action = action.item() |
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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) |
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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 _make_env(self, only_info=False): |
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return wrap_deepmind( |
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self._cfg.env_id, |
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episode_life=self._cfg.is_train, |
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clip_rewards=self._cfg.is_train, |
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pomdp=self._cfg.pomdp, |
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frame_stack=self._cfg.frame_stack, |
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warp_frame=self._cfg.warp_frame, |
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use_ram=self._cfg.use_ram, |
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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 POMDP Atari 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_env_num = cfg.pop('collector_env_num', 1) |
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cfg = copy.deepcopy(cfg) |
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cfg.is_train = True |
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return [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_env_num = cfg.pop('evaluator_env_num', 1) |
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cfg = copy.deepcopy(cfg) |
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cfg.is_train = False |
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return [cfg for _ in range(evaluator_env_num)] |
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@property |
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def observation_space(self) -> gym.spaces.Space: |
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return self._observation_space |
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@property |
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def action_space(self) -> gym.spaces.Space: |
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return self._action_space |
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@property |
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def reward_space(self) -> gym.spaces.Space: |
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return self._reward_space |
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