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| from typing import Any, List, Union, Optional | |
| import time | |
| import copy | |
| import gym | |
| import numpy as np | |
| from ding.envs import BaseEnv, BaseEnvTimestep | |
| from ding.torch_utils import to_ndarray, to_list | |
| from ding.utils import ENV_REGISTRY | |
| import bsuite | |
| from bsuite.utils import gym_wrapper | |
| from bsuite import sweep | |
| class BSuiteEnv(BaseEnv): | |
| def __init__(self, cfg: dict) -> None: | |
| self._cfg = cfg | |
| self._init_flag = False | |
| self.env_id = cfg.env_id | |
| self.env_name = self.env_id.split('/')[0] | |
| def reset(self) -> np.ndarray: | |
| if not self._init_flag: | |
| raw_env = bsuite.load_from_id(bsuite_id=self.env_id) | |
| 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 | |
| ) | |
| 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._eval_episode_return = 0 | |
| obs = self._env.reset() | |
| if obs.shape[0] == 1: | |
| obs = obs[0] | |
| obs = to_ndarray(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 | |
| np.random.seed(self._seed) | |
| def step(self, action: np.ndarray) -> BaseEnvTimestep: | |
| assert isinstance(action, np.ndarray), type(action) | |
| if action.shape[0] == 1: | |
| action = action[0] | |
| obs, rew, done, info = self._env.step(action) | |
| self._eval_episode_return += rew | |
| if done: | |
| info['eval_episode_return'] = self._eval_episode_return | |
| if obs.shape[0] == 1: | |
| obs = obs[0] | |
| obs = to_ndarray(obs) | |
| rew = to_ndarray([rew]) # wrapped to be transfered to a array with shape (1,) | |
| return BaseEnvTimestep(obs, rew, done, info) | |
| def config_info(self) -> dict: | |
| config_info = sweep.SETTINGS[self.env_id] # additional info that are specific to each env configuration | |
| config_info['num_episodes'] = self._env.bsuite_num_episodes | |
| return config_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 __repr__(self) -> str: | |
| return "DI-engine BSuite Env({})".format(self.env_id) | |
| 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)] | |