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import copy |
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from typing import List, Union, Optional |
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import gym |
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import numpy as np |
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from easydict import EasyDict |
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from ding.envs.env.base_env import BaseEnv, BaseEnvTimestep |
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from ding.torch_utils import to_ndarray |
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from ding.utils import ENV_REGISTRY |
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@ENV_REGISTRY.register('cliffwalking') |
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class CliffWalkingEnv(BaseEnv): |
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def __init__(self, cfg: dict) -> None: |
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self._cfg = EasyDict( |
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env_id='CliffWalking', |
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render_mode='rgb_array', |
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max_episode_steps=300, |
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) |
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self._cfg.update(cfg) |
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self._init_flag = False |
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self._replay_path = None |
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self._observation_space = gym.spaces.Box(low=0, high=1, shape=(48, ), dtype=np.float32) |
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self._env = gym.make( |
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"CliffWalking", render_mode=self._cfg.render_mode, max_episode_steps=self._cfg.max_episode_steps |
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) |
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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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def reset(self) -> np.ndarray: |
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if not self._init_flag: |
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self._env = gym.make( |
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"CliffWalking", render_mode=self._cfg.render_mode, max_episode_steps=self._cfg.max_episode_steps |
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) |
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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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dy_seed = self._seed + 100 * np.random.randint(1, 1000) |
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self._env.seed(dy_seed) |
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elif hasattr(self, '_seed'): |
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self._env.seed(self._seed) |
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if self._replay_path is not None: |
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self._env = gym.wrappers.RecordVideo( |
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self._env, |
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video_folder=self._replay_path, |
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episode_trigger=lambda episode_id: True, |
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name_prefix='cliffwalking-{}'.format(id(self)) |
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) |
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obs = self._env.reset() |
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obs_encode = self._encode_obs(obs) |
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self._eval_episode_return = 0. |
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return obs_encode |
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def close(self) -> None: |
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try: |
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self._env.close() |
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del self._env |
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except: |
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pass |
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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(seed) |
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def step(self, action: Union[int, np.ndarray]) -> BaseEnvTimestep: |
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if isinstance(action, np.ndarray): |
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if action.shape == (1, ): |
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action = action.squeeze() |
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action = action.item() |
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obs, reward, done, info = self._env.step(action) |
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obs_encode = self._encode_obs(obs) |
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self._eval_episode_return += reward |
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reward = to_ndarray([reward], dtype=np.float32) |
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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_encode, reward, done, info) |
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def enable_save_replay(self, replay_path: Optional[str] = None) -> None: |
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if replay_path is None: |
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replay_path = './video' |
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self._replay_path = replay_path |
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def random_action(self) -> np.ndarray: |
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random_action = self.action_space.sample() |
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if isinstance(random_action, int): |
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random_action = to_ndarray([random_action], dtype=np.int64) |
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return random_action |
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def _encode_obs(self, obs) -> np.ndarray: |
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onehot = np.zeros(48, dtype=np.float32) |
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onehot[int(obs)] = 1 |
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return onehot |
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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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def __repr__(self) -> str: |
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return "DI-engine CliffWalking Env" |
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