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| import os | |
| from itertools import product | |
| 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_disc_env import MujocoDiscEnv | |
| class MujocoDiscEnvLZ(MujocoDiscEnv): | |
| """ | |
| Overview: | |
| The modified Mujoco environment with manually discretized action space for LightZero's algorithms. | |
| 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. | |
| """ | |
| config = dict( | |
| action_clip=False, | |
| delay_reward_step=0, | |
| replay_path=None, | |
| save_replay_gif=False, | |
| 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 with the given config dictionary. | |
| Arguments: | |
| - cfg (:obj:`dict`): Configuration dictionary. | |
| """ | |
| 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 | |
| def reset(self) -> np.ndarray: | |
| """ | |
| Overview: | |
| Reset the environment. During the reset phase, the original environment will be created, | |
| and at the same time, the action space will be discretized into "each_dim_disc_size" bins. | |
| Returns: | |
| - info_dict (:obj:`Dict[str, Any]`): Including observation, action_mask, and to_play label. | |
| """ | |
| if not self._init_flag: | |
| self._env = self._make_env() | |
| self._env.observation_space.dtype = np.float32 | |
| 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) | |
| action_mask = np.ones(self.K, 'int8') | |
| obs = {'observation': obs, 'action_mask': action_mask, 'to_play': -1} | |
| return obs | |
| def step(self, action: Union[np.ndarray, list]) -> BaseEnvTimestep: | |
| """ | |
| Overview: | |
| Take an action in the environment. During the step phase, the environment first converts the discrete action into a continuous action, | |
| and then passes it into the original environment. | |
| Arguments: | |
| - action (:obj:`Union[np.ndarray, list]`): Discrete action to be taken in the environment. | |
| Returns: | |
| - BaseEnvTimestep (:obj:`BaseEnvTimestep`): A tuple containing observation, reward, done, and info. | |
| """ | |
| # 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_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 = np.ones(self._action_space.n, 'int8') | |
| obs = {'observation': obs, 'action_mask': action_mask, 'to_play': -1} | |
| return BaseEnvTimestep(obs, rew, done, info) | |
| def __repr__(self) -> str: | |
| """ | |
| Overview: | |
| Represent the environment instance as a string. | |
| Returns: | |
| - repr_str (:obj:`str`): Representation string of the environment instance. | |
| """ | |
| return "LightZero modified Mujoco Env({}) with manually discretized action space".format(self._cfg.env_name) | |