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| import copy | |
| import os | |
| from datetime import datetime | |
| from itertools import product | |
| import gymnasium as gym | |
| import numpy as np | |
| from itertools import product | |
| from ding.envs import BaseEnvTimestep | |
| from ding.envs import ObsPlusPrevActRewWrapper | |
| from ding.envs.common import affine_transform | |
| from ding.torch_utils import to_ndarray | |
| from ding.utils import ENV_REGISTRY | |
| from easydict import EasyDict | |
| from zoo.box2d.lunarlander.envs.lunarlander_env import LunarLanderEnv | |
| class LunarLanderDiscEnv(LunarLanderEnv): | |
| """ | |
| Overview: | |
| The modified LunarLander environment with manually discretized action space. For each dimension, it equally divides the | |
| original continuous action into ``each_dim_disc_size`` bins and uses their Cartesian product to obtain | |
| handcrafted discrete actions. | |
| """ | |
| def default_config(cls: type) -> EasyDict: | |
| """ | |
| Overview: | |
| Get the default configuration of the LunarLander environment. | |
| Returns: | |
| - cfg (:obj:`EasyDict`): Default configuration dictionary. | |
| """ | |
| cfg = EasyDict(copy.deepcopy(cls.config)) | |
| cfg.cfg_type = cls.__name__ + 'Dict' | |
| return cfg | |
| config = dict( | |
| # (str) The gym environment name. | |
| env_name="LunarLander-v2", | |
| # (int) The number of bins for each dimension of the action space. | |
| each_dim_disc_size=4, | |
| # (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, | |
| # (str or None) The path to save the replay. If None, the replay will not be saved. | |
| replay_path=None, | |
| # (bool) If True, the action will be scaled. | |
| act_scale=True, | |
| # (int) The maximum number of steps for each episode during collection. | |
| collect_max_episode_steps=int(1.08e5), | |
| # (int) The maximum number of steps for each episode during evaluation. | |
| eval_max_episode_steps=int(1.08e5), | |
| ) | |
| def __init__(self, cfg: dict) -> None: | |
| """ | |
| Overview: | |
| Initialize the LunarLander environment with the given config dictionary. | |
| Arguments: | |
| - cfg (:obj:`dict`): Configuration dictionary. | |
| """ | |
| self._cfg = cfg | |
| self._init_flag = False | |
| # env_name: LunarLander-v2, LunarLanderContinuous-v2 | |
| self._env_name = cfg.env_name | |
| self._replay_path = cfg.replay_path | |
| self._replay_path_gif = cfg.replay_path_gif | |
| self._save_replay_gif = cfg.save_replay_gif | |
| self._save_replay_count = 0 | |
| if 'Continuous' in self._env_name: | |
| self._act_scale = cfg.act_scale # act_scale only works in continuous env | |
| else: | |
| self._act_scale = False | |
| 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 = gym.make(self._cfg.env_name, render_mode="rgb_array") | |
| if self._replay_path is not None: | |
| timestamp = datetime.now().strftime("%Y%m%d%H%M%S") | |
| video_name = f'{self._env.spec.id}-video-{timestamp}' | |
| self._env = gym.wrappers.RecordVideo( | |
| self._env, | |
| video_folder=self._replay_path, | |
| episode_trigger=lambda episode_id: True, | |
| name_prefix=video_name | |
| ) | |
| if hasattr(self._cfg, 'obs_plus_prev_action_reward') and self._cfg.obs_plus_prev_action_reward: | |
| self._env = ObsPlusPrevActRewWrapper(self._env) | |
| self._observation_space = self._env.observation_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._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._seed = self._seed + np_seed | |
| obs, _ = self._env.reset(seed=self._seed) # using the reset method of Gymnasium env | |
| elif hasattr(self, '_seed'): | |
| obs, _ = self._env.reset(seed=self._seed) | |
| else: | |
| obs, _ = self._env.reset() | |
| obs = to_ndarray(obs) | |
| self._eval_episode_return = 0 | |
| if self._save_replay_gif: | |
| self._frames = [] | |
| # disc_to_cont: transform discrete action index to original continuous action | |
| self._raw_action_space = self._env.action_space | |
| 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 dim in range(self.m)])) | |
| # 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: np.ndarray) -> 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:`np.ndarray`): Discrete action to be taken in the environment. | |
| Returns: | |
| - BaseEnvTimestep (:obj:`BaseEnvTimestep`): A tuple containing observation, reward, done, and info. | |
| """ | |
| action = [-1 + 2 / self.n * k for k in self.disc_to_cont[int(action)]] | |
| action = to_ndarray(action) | |
| if action.shape == (1, ): | |
| action = action.item() # 0-dim array | |
| if self._act_scale: | |
| action = affine_transform(action, min_val=-1, max_val=1) | |
| if self._save_replay_gif: | |
| self._frames.append(self._env.render()) | |
| obs, rew, terminated, truncated, info = self._env.step(action) | |
| done = terminated or truncated | |
| action_mask = np.ones(self._action_space.n, 'int8') | |
| obs = {'observation': obs, 'action_mask': action_mask, 'to_play': -1} | |
| self._eval_episode_return += rew | |
| if done: | |
| info['eval_episode_return'] = self._eval_episode_return | |
| if self._save_replay_gif: | |
| if not os.path.exists(self._replay_path_gif): | |
| os.makedirs(self._replay_path_gif) | |
| timestamp = datetime.now().strftime("%Y%m%d%H%M%S") | |
| path = os.path.join( | |
| self._replay_path_gif, | |
| '{}_episode_{}_seed{}_{}.gif'.format(self._env_name, self._save_replay_count, self._seed, timestamp) | |
| ) | |
| self.display_frames_as_gif(self._frames, path) | |
| print(f'save episode {self._save_replay_count} in {self._replay_path_gif}!') | |
| self._save_replay_count += 1 | |
| obs = to_ndarray(obs) | |
| rew = to_ndarray([rew]).astype(np.float32) # wrapped to be transferred to an array with shape (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 LunarLander Env (with manually discretized action space)" | |