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from typing import Any, List, Union, Optional |
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import time |
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import copy |
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import gym |
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import numpy as np |
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from ding.envs import BaseEnv, BaseEnvTimestep |
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from ding.torch_utils import to_ndarray, to_list |
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from ding.utils import ENV_REGISTRY |
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import bsuite |
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from bsuite.utils import gym_wrapper |
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from bsuite import sweep |
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@ENV_REGISTRY.register('bsuite') |
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class BSuiteEnv(BaseEnv): |
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def __init__(self, cfg: dict) -> None: |
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self._cfg = cfg |
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self._init_flag = False |
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self.env_id = cfg.env_id |
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self.env_name = self.env_id.split('/')[0] |
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def reset(self) -> np.ndarray: |
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if not self._init_flag: |
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raw_env = bsuite.load_from_id(bsuite_id=self.env_id) |
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self._env = gym_wrapper.GymFromDMEnv(raw_env) |
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self._observation_space = self._env.observation_space |
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self._action_space = self._env.action_space |
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self._reward_space = gym.spaces.Box( |
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low=self._env.reward_range[0], high=self._env.reward_range[1], shape=(1, ), dtype=np.float64 |
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) |
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self._init_flag = True |
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if hasattr(self, '_seed') and hasattr(self, '_dynamic_seed') and self._dynamic_seed: |
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np_seed = 100 * np.random.randint(1, 1000) |
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self._env.seed(self._seed + np_seed) |
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elif hasattr(self, '_seed'): |
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self._env.seed(self._seed) |
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self._eval_episode_return = 0 |
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obs = self._env.reset() |
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if obs.shape[0] == 1: |
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obs = obs[0] |
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obs = to_ndarray(obs).astype(np.float32) |
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return obs |
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def close(self) -> None: |
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if self._init_flag: |
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self._env.close() |
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self._init_flag = False |
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def seed(self, seed: int, dynamic_seed: bool = True) -> None: |
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self._seed = seed |
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self._dynamic_seed = dynamic_seed |
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np.random.seed(self._seed) |
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def step(self, action: np.ndarray) -> BaseEnvTimestep: |
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assert isinstance(action, np.ndarray), type(action) |
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if action.shape[0] == 1: |
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action = action[0] |
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obs, rew, done, info = self._env.step(action) |
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self._eval_episode_return += rew |
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if done: |
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info['eval_episode_return'] = self._eval_episode_return |
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if obs.shape[0] == 1: |
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obs = obs[0] |
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obs = to_ndarray(obs) |
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rew = to_ndarray([rew]) |
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return BaseEnvTimestep(obs, rew, done, info) |
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def config_info(self) -> dict: |
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config_info = sweep.SETTINGS[self.env_id] |
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config_info['num_episodes'] = self._env.bsuite_num_episodes |
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return config_info |
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def random_action(self) -> np.ndarray: |
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random_action = self.action_space.sample() |
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random_action = to_ndarray([random_action], dtype=np.int64) |
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return random_action |
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@property |
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def observation_space(self) -> gym.spaces.Space: |
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return self._observation_space |
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@property |
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def action_space(self) -> gym.spaces.Space: |
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return self._action_space |
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@property |
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def reward_space(self) -> gym.spaces.Space: |
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return self._reward_space |
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def __repr__(self) -> str: |
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return "DI-engine BSuite Env({})".format(self.env_id) |
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@staticmethod |
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def create_collector_env_cfg(cfg: dict) -> List[dict]: |
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collector_env_num = cfg.pop('collector_env_num') |
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cfg = copy.deepcopy(cfg) |
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cfg.is_train = True |
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return [cfg for _ in range(collector_env_num)] |
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@staticmethod |
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def create_evaluator_env_cfg(cfg: dict) -> List[dict]: |
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evaluator_env_num = cfg.pop('evaluator_env_num') |
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cfg = copy.deepcopy(cfg) |
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cfg.is_train = False |
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return [cfg for _ in range(evaluator_env_num)] |
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