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| from typing import Any, List, Union, Optional | |
| from easydict import EasyDict | |
| import time | |
| import gym | |
| import numpy as np | |
| from ding.envs import BaseEnv, BaseEnvTimestep | |
| from ding.envs.common.env_element import EnvElement, EnvElementInfo | |
| from ding.torch_utils import to_ndarray, to_list | |
| from ding.utils import ENV_REGISTRY, deep_merge_dicts | |
| class ProcgenEnv(BaseEnv): | |
| #If control_level is True, you can control the specific level of the generated environment by controlling start_level and num_level. | |
| config = dict( | |
| control_level=True, | |
| start_level=0, | |
| num_levels=0, | |
| env_id='coinrun', | |
| ) | |
| def __init__(self, cfg: dict) -> None: | |
| cfg = deep_merge_dicts(EasyDict(self.config), cfg) | |
| self._cfg = cfg | |
| self._seed = 0 | |
| self._init_flag = False | |
| self._observation_space = gym.spaces.Box( | |
| low=np.zeros(shape=(3, 64, 64)), high=np.ones(shape=(3, 64, 64)) * 255, shape=(3, 64, 64), dtype=np.float32 | |
| ) | |
| self._action_space = gym.spaces.Discrete(15) | |
| self._reward_space = gym.spaces.Box(low=float("-inf"), high=float("inf"), shape=(1, ), dtype=np.float32) | |
| self._control_level = self._cfg.control_level | |
| self._start_level = self._cfg.start_level | |
| self._num_levels = self._cfg.num_levels | |
| self._env_name = 'procgen:procgen-' + self._cfg.env_id + '-v0' | |
| # In procgen envs, we use seed to control level, and fix the numpy seed to 0 | |
| np.random.seed(0) | |
| def reset(self) -> np.ndarray: | |
| if not self._init_flag: | |
| if self._control_level: | |
| self._env = gym.make(self._env_name, start_level=self._start_level, num_levels=self._num_levels) | |
| else: | |
| self._env = gym.make(self._env_name, start_level=0, num_levels=1) | |
| 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.close() | |
| if self._control_level: | |
| self._env = gym.make(self._env_name, start_level=self._start_level, num_levels=self._num_levels) | |
| else: | |
| self._env = gym.make(self._env_name, start_level=self._seed + np_seed, num_levels=1) | |
| elif hasattr(self, '_seed'): | |
| self._env.close() | |
| if self._control_level: | |
| self._env = gym.make(self._env_name, start_level=self._start_level, num_levels=self._num_levels) | |
| else: | |
| self._env = gym.make(self._env_name, start_level=self._seed, num_levels=1) | |
| self._eval_episode_return = 0 | |
| obs = self._env.reset() | |
| obs = to_ndarray(obs) | |
| obs = np.transpose(obs, (2, 0, 1)) | |
| obs = obs.astype(np.float32) | |
| 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 | |
| def step(self, action: np.ndarray) -> BaseEnvTimestep: | |
| assert isinstance(action, np.ndarray), type(action) | |
| if action.shape == (1, ): | |
| action = action.squeeze() # 0-dim array | |
| obs, rew, done, info = self._env.step(action) | |
| self._eval_episode_return += rew | |
| if done: | |
| info['eval_episode_return'] = self._eval_episode_return | |
| obs = to_ndarray(obs) | |
| obs = np.transpose(obs, (2, 0, 1)) | |
| obs = obs.astype(np.float32) | |
| rew = to_ndarray([rew]) # wrapped to be transfered to a array with shape (1,) | |
| rew = rew.astype(np.float32) | |
| return BaseEnvTimestep(obs, rew, bool(done), info) | |
| 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 __repr__(self) -> str: | |
| return "DI-engine CoinRun Env" | |
| def enable_save_replay(self, replay_path: Optional[str] = None) -> None: | |
| if replay_path is None: | |
| replay_path = './video' | |
| self._replay_path = replay_path | |
| self._env = gym.wrappers.Monitor( | |
| self._env, self._replay_path, video_callable=lambda episode_id: True, force=True | |
| ) | |