import copy import os from datetime import datetime from typing import Union, Optional, Dict, List import gymnasium as gym import matplotlib.pyplot as plt import numpy as np import bsuite from bsuite import sweep from bsuite.utils import gym_wrapper from ding.envs import BaseEnv, BaseEnvTimestep from ding.torch_utils import to_ndarray from ding.utils import ENV_REGISTRY from easydict import EasyDict from matplotlib import animation @ENV_REGISTRY.register('bsuite_lightzero') class BSuiteEnv(BaseEnv): """ LightZero version of the Bsuite environment. This class includes methods for resetting, closing, and stepping through the environment, as well as seeding for reproducibility, saving replay videos, and generating random actions. It also includes properties for accessing the observation space, action space, and reward space of the environment. """ config = dict( # (str) The gym environment name. env_name='memory_len/9', # (bool) If True, save the replay as a gif file. # Due to the definition of the environment, rendering images of certain sub-environments are meaningless. 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, # 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, ) @classmethod def default_config(cls: type) -> EasyDict: """ Overview: Return the default configuration of the class. Returns: - cfg (:obj:`EasyDict`): Default configuration dict. """ cfg = EasyDict(copy.deepcopy(cls.config)) cfg.cfg_type = cls.__name__ + 'Dict' return cfg def __init__(self, cfg: dict = {}) -> None: """ Initialize the environment with a configuration dictionary. Sets up spaces for observations, actions, and rewards. """ self._cfg = cfg self._init_flag = False 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 def reset(self) -> Dict[str, np.ndarray]: """ Reset the environment. If it hasn't been initialized yet, this method also handles that. It also handles seeding if necessary. Returns the first observation. """ if not self._init_flag: raw_env = bsuite.load_from_id(bsuite_id=self._env_name) self._env = gym_wrapper.GymFromDMEnv(raw_env) 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.float64 ) 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 ) 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) self._observation_space = self._env.observation_space obs = self._env.reset() if obs.shape[0] == 1: obs = obs[0] obs = to_ndarray(obs).astype(np.float32) self._eval_episode_return = 0 if self._save_replay_gif: self._frames = [] action_mask = np.ones(self.action_space.n, 'int8') obs = {'observation': obs, 'action_mask': action_mask, 'to_play': -1} return obs def step(self, action: np.ndarray) -> 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:`np.ndarray`): 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._save_replay_gif: self._frames.append(self._env.render(mode='rgb_array')) obs, rew, done, info = self._env.step(action) 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._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 if obs.shape[0] == 1: obs = obs[0] obs = to_ndarray(obs) rew = to_ndarray([rew]) # wrapped to be transfered to an array with shape (1,) 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 config_info(self) -> dict: config_info = sweep.SETTINGS[self._env_name] # additional info that are specific to each env configuration config_info['num_episodes'] = self._env.bsuite_num_episodes return config_info def close(self) -> None: """ Close the environment, and set the initialization flag to False. """ if self._init_flag: self._env.close() self._init_flag = False def seed(self, seed: int, dynamic_seed: bool = True) -> None: """ Set the seed for the environment's random number generator. Can handle both static and dynamic seeding. """ self._seed = seed self._dynamic_seed = dynamic_seed np.random.seed(self._seed) def enable_save_replay(self, replay_path: Optional[str] = None) -> None: """ Enable the saving of replay videos. If no replay path is given, a default is used. """ if replay_path is None: replay_path = './video' self._replay_path = replay_path @staticmethod def display_frames_as_gif(frames: list, path: str) -> None: patch = plt.imshow(frames[0]) plt.axis('off') def animate(i): patch.set_data(frames[i]) anim = animation.FuncAnimation(plt.gcf(), animate, frames=len(frames), interval=5) anim.save(path, writer='imagemagick', fps=20) def random_action(self) -> np.ndarray: """ Generate a random action using the action space's sample method. Returns a numpy array containing the action. """ random_action = self.action_space.sample() random_action = to_ndarray([random_action], dtype=np.int64) return random_action @property def observation_space(self) -> gym.spaces.Space: """ Property to access the observation space of the environment. """ return self._observation_space @property def action_space(self) -> gym.spaces.Space: """ Property to access the action space of the environment. """ return self._action_space @property def reward_space(self) -> gym.spaces.Space: """ Property to access the reward space of the environment. """ return self._reward_space @staticmethod def create_collector_env_cfg(cfg: dict) -> List[dict]: collector_env_num = cfg.pop('collector_env_num') cfg = copy.deepcopy(cfg) cfg.is_train = True return [cfg for _ in range(collector_env_num)] @staticmethod def create_evaluator_env_cfg(cfg: dict) -> List[dict]: evaluator_env_num = cfg.pop('evaluator_env_num') cfg = copy.deepcopy(cfg) cfg.is_train = False return [cfg for _ in range(evaluator_env_num)] def __repr__(self) -> str: """ String representation of the environment. """ return "LightZero BSuite Env({})".format(self._env_name)