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| from typing import Any, List, Union, Optional | |
| from collections import namedtuple | |
| from easydict import EasyDict | |
| import copy | |
| import os | |
| import time | |
| import gymnasium as gym | |
| import numpy as np | |
| from matplotlib import animation | |
| import matplotlib.pyplot as plt | |
| from minigrid.wrappers import FlatObsWrapper, RGBImgPartialObsWrapper, ImgObsWrapper | |
| from .minigrid_wrapper import ViewSizeWrapper | |
| from ding.envs import ObsPlusPrevActRewWrapper | |
| from ding.envs import BaseEnv, BaseEnvTimestep | |
| from ding.torch_utils import to_ndarray, to_list | |
| from ding.utils import ENV_REGISTRY | |
| class MiniGridEnv(BaseEnv): | |
| config = dict( | |
| env_id='MiniGrid-KeyCorridorS3R3-v0', | |
| flat_obs=True, | |
| ) | |
| def default_config(cls: type) -> EasyDict: | |
| cfg = EasyDict(copy.deepcopy(cls.config)) | |
| cfg.cfg_type = cls.__name__ + 'Dict' | |
| return cfg | |
| def __init__(self, cfg: dict) -> None: | |
| self._cfg = cfg | |
| self._init_flag = False | |
| self._env_id = cfg.env_id | |
| self._flat_obs = cfg.flat_obs | |
| self._save_replay = False | |
| self._max_step = cfg.max_step | |
| def reset(self) -> np.ndarray: | |
| if not self._init_flag: | |
| if self._save_replay: | |
| self._env = gym.make(self._env_id, render_mode="rgb_array") # using the Gymnasium make method | |
| else: | |
| self._env = gym.make(self._env_id) | |
| if self._env_id in ['MiniGrid-AKTDT-13x13-v0' or 'MiniGrid-AKTDT-13x13-1-v0']: | |
| # customize the agent field of view size, note this must be an odd number | |
| # This also related to the observation space, see gym_minigrid.wrappers for more details | |
| self._env = ViewSizeWrapper(self._env, agent_view_size=5) | |
| if self._env_id == 'MiniGrid-AKTDT-7x7-1-v0': | |
| self._env = ViewSizeWrapper(self._env, agent_view_size=3) | |
| if self._flat_obs: | |
| self._env = FlatObsWrapper(self._env) | |
| # self._env = RGBImgPartialObsWrapper(self._env) | |
| # self._env = ImgObsWrapper(self._env) | |
| if hasattr(self._cfg, 'obs_plus_prev_action_reward') and self._cfg.obs_plus_prev_action_reward: | |
| self._env = ObsPlusPrevActRewWrapper(self._env) | |
| self._init_flag = True | |
| if self._flat_obs: | |
| self._observation_space = gym.spaces.Box(0, 1, shape=(2835, ), dtype=np.float32) | |
| else: | |
| self._observation_space = self._env.observation_space | |
| # to be compatiable with subprocess env manager | |
| if isinstance(self._observation_space, gym.spaces.Dict): | |
| self._observation_space['obs'].dtype = np.dtype('float32') | |
| else: | |
| self._observation_space.dtype = np.dtype('float32') | |
| 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._eval_episode_return = 0 | |
| 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._current_step = 0 | |
| if self._save_replay: | |
| self._frames = [] | |
| 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: np.ndarray) -> BaseEnvTimestep: | |
| assert isinstance(action, np.ndarray), type(action) | |
| if action.shape == (1, ): | |
| action = action.squeeze() # 0-dim array | |
| if self._save_replay: | |
| self._frames.append(self._env.render()) | |
| # using the step method of Gymnasium env, return is (observation, reward, terminated, truncated, info) | |
| obs, rew, done, _, info = self._env.step(action) | |
| rew = float(rew) | |
| self._eval_episode_return += rew | |
| self._current_step += 1 | |
| if self._current_step >= self._max_step: | |
| done = True | |
| if done: | |
| info['eval_episode_return'] = self._eval_episode_return | |
| info['current_step'] = self._current_step | |
| info['max_step'] = self._max_step | |
| if self._save_replay: | |
| path = os.path.join( | |
| self._replay_path, '{}_episode_{}.gif'.format(self._env_id, self._save_replay_count) | |
| ) | |
| self.display_frames_as_gif(self._frames, path) | |
| self._save_replay_count += 1 | |
| obs = to_ndarray(obs) | |
| rew = to_ndarray([rew]) # wrapped to be transferred to a array with shape (1,) | |
| return BaseEnvTimestep(obs, rew, done, info) | |
| def random_action(self) -> np.ndarray: | |
| random_action = self.action_space.sample() | |
| random_action = to_ndarray([random_action], dtype=np.int64) | |
| return random_action | |
| 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 | |
| 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)] | |
| 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: | |
| return "DI-engine MiniGrid Env({})".format(self._cfg.env_id) | |
| def enable_save_replay(self, replay_path: Optional[str] = None) -> None: | |
| if replay_path is None: | |
| replay_path = './video' | |
| self._save_replay = True | |
| self._replay_path = replay_path | |
| self._save_replay_count = 0 | |
| 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) | |