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| import copy | |
| import os | |
| from itertools import product | |
| from typing import Union, List, Optional | |
| import gym | |
| import numpy as np | |
| from easydict import EasyDict | |
| from ding.envs import BaseEnv, BaseEnvTimestep | |
| from ding.envs.common import save_frames_as_gif | |
| from ding.torch_utils import to_ndarray | |
| from ding.utils import ENV_REGISTRY | |
| from .mujoco_wrappers import wrap_mujoco | |
| class MujocoDiscEnv(BaseEnv): | |
| """ | |
| Overview: | |
| The modified Mujoco environment with manually discretized action space. For each dimension, equally dividing the | |
| original continuous action into ``each_dim_disc_size`` bins and using their Cartesian product to obtain | |
| handcrafted discrete actions. | |
| """ | |
| def default_config(cls: type) -> EasyDict: | |
| cfg = EasyDict(copy.deepcopy(cls.config)) | |
| cfg.cfg_type = cls.__name__ + 'Dict' | |
| return cfg | |
| config = dict( | |
| action_clip=False, | |
| delay_reward_step=0, | |
| replay_path=None, | |
| save_replay_gif=False, | |
| replay_path_gif=None, | |
| ) | |
| def __init__(self, cfg: dict) -> None: | |
| self._cfg = cfg | |
| self._action_clip = cfg.action_clip | |
| self._delay_reward_step = cfg.delay_reward_step | |
| self._init_flag = False | |
| self._replay_path = None | |
| self._replay_path_gif = cfg.replay_path_gif | |
| self._save_replay_gif = cfg.save_replay_gif | |
| def reset(self) -> np.ndarray: | |
| if not self._init_flag: | |
| self._env = self._make_env() | |
| self._env.observation_space.dtype = np.float32 # To unify the format of envs in DI-engine | |
| self._observation_space = self._env.observation_space | |
| self._raw_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)) | |
| ) | |
| if self._save_replay_gif: | |
| self._frames = [] | |
| obs = self._env.reset() | |
| obs = to_ndarray(obs).astype('float32') | |
| # disc_to_cont: transform discrete action index to original continuous action | |
| self.m = self._raw_action_space.shape[0] | |
| self.n = self._cfg.each_dim_disc_size | |
| self.K = self.n ** self.m | |
| self.disc_to_cont = list(product(*[list(range(self.n)) for _ in range(self.m)])) | |
| self._eval_episode_return = 0. | |
| # the modified discrete action space | |
| self._action_space = gym.spaces.Discrete(self.K) | |
| 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: Union[np.ndarray, list]) -> BaseEnvTimestep: | |
| # disc_to_cont: transform discrete action index to original continuous action | |
| action = [-1 + 2 / self.n * k for k in self.disc_to_cont[int(action)]] | |
| action = to_ndarray(action) | |
| if self._save_replay_gif: | |
| self._frames.append(self._env.render(mode='rgb_array')) | |
| if self._action_clip: | |
| action = np.clip(action, -1, 1) | |
| obs, rew, done, info = self._env.step(action) | |
| self._eval_episode_return += rew | |
| if done: | |
| if self._save_replay_gif: | |
| path = os.path.join( | |
| self._replay_path_gif, '{}_episode_{}.gif'.format(self._cfg.env_id, self._save_replay_count) | |
| ) | |
| save_frames_as_gif(self._frames, path) | |
| self._save_replay_count += 1 | |
| info['eval_episode_return'] = self._eval_episode_return | |
| obs = to_ndarray(obs).astype(np.float32) | |
| rew = to_ndarray([rew]).astype(np.float32) | |
| return BaseEnvTimestep(obs, rew, done, info) | |
| def _make_env(self): | |
| return wrap_mujoco( | |
| self._cfg.env_id, | |
| norm_obs=self._cfg.get('norm_obs', None), | |
| norm_reward=self._cfg.get('norm_reward', None), | |
| delay_reward_step=self._delay_reward_step | |
| ) | |
| def enable_save_replay(self, replay_path: Optional[str] = None) -> None: | |
| if replay_path is None: | |
| replay_path = './video' | |
| self._replay_path = replay_path | |
| self._save_replay = True | |
| self._save_replay_count = 0 | |
| def random_action(self) -> np.ndarray: | |
| return self.action_space.sample() | |
| def __repr__(self) -> str: | |
| return "DI-engine modified Mujoco Env({}) with manually discretized action space".format(self._cfg.env_id) | |
| def create_collector_env_cfg(cfg: dict) -> List[dict]: | |
| collector_cfg = copy.deepcopy(cfg) | |
| collector_env_num = collector_cfg.pop('collector_env_num', 1) | |
| return [collector_cfg for _ in range(collector_env_num)] | |
| def create_evaluator_env_cfg(cfg: dict) -> List[dict]: | |
| evaluator_cfg = copy.deepcopy(cfg) | |
| evaluator_env_num = evaluator_cfg.pop('evaluator_env_num', 1) | |
| evaluator_cfg.norm_reward.use_norm = False | |
| return [evaluator_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 | |