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from typing import Any, List, Union, Optional |
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import time |
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import os |
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import imageio |
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import gym |
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import copy |
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import numpy as np |
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from easydict import EasyDict |
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from rocket_recycling.rocket import Rocket |
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from ding.envs import BaseEnv, BaseEnvTimestep |
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from ding.torch_utils import to_ndarray, to_list |
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from ding.utils import ENV_REGISTRY |
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from ding.envs import ObsPlusPrevActRewWrapper |
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@ENV_REGISTRY.register('rocket', force_overwrite=True) |
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class RocketEnv(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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self._save_replay = False |
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self._observation_space = gym.spaces.Box(low=float("-inf"), high=float("inf"), shape=(8, ), dtype=np.float32) |
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self._action_space = gym.spaces.Discrete(9) |
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self._action_space.seed(0) |
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self._reward_space = gym.spaces.Box(low=float("-inf"), high=float("inf"), shape=(1, ), dtype=np.float32) |
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def reset(self) -> np.ndarray: |
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if not self._init_flag: |
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self._env = Rocket(task=self._cfg.task, max_steps=self._cfg.max_steps) |
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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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np_seed = 100 * np.random.randint(1, 1000) |
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self._env.seed(self._seed + np_seed) |
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self._action_space.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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self._action_space.seed(self._seed) |
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self._eval_episode_return = 0 |
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obs = self._env.reset() |
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obs = to_ndarray(obs) |
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if self._save_replay: |
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self._frames = [] |
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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: Union[int, np.ndarray]) -> BaseEnvTimestep: |
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if isinstance(action, np.ndarray) and action.shape == (1, ): |
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action = action.squeeze() |
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obs, rew, done, info = self._env.step(action) |
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self._env.render() |
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self._eval_episode_return += rew |
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if self._save_replay: |
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self._frames.extend(self._env.render()) |
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if done: |
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info['eval_episode_return'] = self._eval_episode_return |
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if self._save_replay: |
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path = os.path.join(self._replay_path, '{}_episode.gif'.format(self._save_replay_count)) |
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self.display_frames_as_gif(self._frames, path) |
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self._save_replay_count += 1 |
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obs = to_ndarray(obs) |
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rew = to_ndarray([rew]).astype(np.float32) |
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return BaseEnvTimestep(obs, rew, 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._save_replay = True |
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if not os.path.exists(replay_path): |
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os.makedirs(replay_path) |
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self._replay_path = replay_path |
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self._save_replay_count = 0 |
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def random_action(self) -> np.ndarray: |
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random_action = self.action_space.sample() |
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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 clone(self, caller: str) -> 'RocketEnv': |
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return RocketEnv(copy.deepcopy(self._cfg)) |
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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 Rocket Env" |
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@staticmethod |
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def display_frames_as_gif(frames: list, path: str) -> None: |
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imageio.mimsave(path, frames, fps=20) |
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