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| import os | |
| from typing import Union | |
| import gymnasium as gym | |
| import numpy as np | |
| from ding.envs import BaseEnvTimestep | |
| from ding.envs.common import save_frames_as_gif | |
| from ding.torch_utils import to_ndarray | |
| from ding.utils import ENV_REGISTRY | |
| from dizoo.mujoco.envs.mujoco_env import MujocoEnv | |
| class MujocoEnvLZ(MujocoEnv): | |
| """ | |
| Overview: | |
| The modified MuJoCo environment with continuous action space for LightZero's algorithms. | |
| """ | |
| config = dict( | |
| stop_value=int(1e6), | |
| action_clip=False, | |
| delay_reward_step=0, | |
| # replay_path (str or None): The path to save the replay video. If None, the replay will not be saved. | |
| # Only effective when env_manager.type is 'base'. | |
| replay_path=None, | |
| # (bool) If True, save the replay as a gif file. | |
| save_replay_gif=False, | |
| # (str or None) The path to save the replay gif. If None, the replay gif will not be saved. | |
| replay_path_gif=None, | |
| action_bins_per_branch=None, | |
| norm_obs=dict(use_norm=False, ), | |
| norm_reward=dict(use_norm=False, ), | |
| ) | |
| def __init__(self, cfg: dict) -> None: | |
| """ | |
| Overview: | |
| Initialize the MuJoCo environment. | |
| Arguments: | |
| - cfg (:obj:`dict`): Configuration dict. The dict should include keys like 'env_name', 'replay_path', etc. | |
| """ | |
| super().__init__(cfg) | |
| self._cfg = cfg | |
| # We use env_name to indicate the env_id in LightZero. | |
| self._cfg.env_id = self._cfg.env_name | |
| 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 | |
| self._action_bins_per_branch = cfg.action_bins_per_branch | |
| def reset(self) -> np.ndarray: | |
| """ | |
| Overview: | |
| Reset the environment and return the initial observation. | |
| Returns: | |
| - obs (:obj:`np.ndarray`): The initial observation after resetting. | |
| """ | |
| if not self._init_flag: | |
| self._env = self._make_env() | |
| 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._env.observation_space.dtype = np.float32 | |
| 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) | |
| obs = self._env.reset() | |
| obs = to_ndarray(obs).astype('float32') | |
| self._eval_episode_return = 0. | |
| action_mask = None | |
| obs = {'observation': obs, 'action_mask': action_mask, 'to_play': -1} | |
| return obs | |
| def step(self, action: Union[np.ndarray, list]) -> BaseEnvTimestep: | |
| """ | |
| Overview: | |
| Perform a step in the environment using the provided action, and return the next state of the environment. | |
| The next state is encapsulated in a BaseEnvTimestep object, which includes the new observation, reward, | |
| done flag, and info dictionary. | |
| Arguments: | |
| - action (:obj:`Union[np.ndarray, list]`): The action to be performed in the environment. | |
| Returns: | |
| - timestep (:obj:`BaseEnvTimestep`): An object containing the new observation, reward, done flag, | |
| and info dictionary. | |
| .. note:: | |
| - The cumulative reward (`_eval_episode_return`) is updated with the reward obtained in this step. | |
| - If the episode ends (done is True), the total reward for the episode is stored in the info dictionary | |
| under the key 'eval_episode_return'. | |
| - An action mask is created with ones, which represents the availability of each action in the action space. | |
| - Observations are returned in a dictionary format containing 'observation', 'action_mask', and 'to_play'. | |
| """ | |
| if self._action_bins_per_branch: | |
| action = self.map_action(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_name, 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) | |
| action_mask = None | |
| obs = {'observation': obs, 'action_mask': action_mask, 'to_play': -1} | |
| return BaseEnvTimestep(obs, rew, done, info) | |
| def __repr__(self) -> str: | |
| """ | |
| String representation of the environment. | |
| """ | |
| return "LightZero Mujoco Env({})".format(self._cfg.env_name) | |