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| from typing import Any, List, Union, Optional | |
| import time | |
| import gym | |
| import numpy as np | |
| from ding.envs import BaseEnv, BaseEnvTimestep, FrameStackWrapper | |
| from ding.torch_utils import to_ndarray, to_list | |
| from ding.envs.common.common_function import affine_transform | |
| from ding.utils import ENV_REGISTRY | |
| class BipedalWalkerEnv(BaseEnv): | |
| def __init__(self, cfg: dict) -> None: | |
| self._cfg = cfg | |
| self._init_flag = False | |
| self._act_scale = cfg.act_scale | |
| self._rew_clip = cfg.rew_clip | |
| if "replay_path" in cfg: | |
| self._replay_path = cfg.replay_path | |
| else: | |
| self._replay_path = None | |
| def reset(self) -> np.ndarray: | |
| if not self._init_flag: | |
| self._env = gym.make('BipedalWalker-v3') | |
| 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 | |
| ) | |
| 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) | |
| if self._replay_path is not None: | |
| 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._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 render(self) -> None: | |
| self._env.render() | |
| 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) | |
| if action.shape == (1, ): | |
| action = action.squeeze() # 0-dim array | |
| if self._act_scale: | |
| action = affine_transform(action, min_val=self.action_space.low, max_val=self.action_space.high) | |
| obs, rew, done, info = self._env.step(action) | |
| self._eval_episode_return += rew | |
| if self._rew_clip: | |
| rew = max(-10, rew) | |
| rew = np.float32(rew) | |
| if done: | |
| info['eval_episode_return'] = self._eval_episode_return | |
| obs = to_ndarray(obs).astype(np.float32) | |
| rew = to_ndarray([rew]) # wrapped to be transfered 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() | |
| if isinstance(random_action, np.ndarray): | |
| pass | |
| elif isinstance(random_action, int): | |
| random_action = to_ndarray([random_action], dtype=np.int64) | |
| return random_action | |
| 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 | |
| def __repr__(self) -> str: | |
| return "DI-engine BipedalWalker Env" | |