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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
@ENV_REGISTRY.register('bsuite')
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
@property
def observation_space(self) -> gym.spaces.Space:
return self._observation_space
@property
def action_space(self) -> gym.spaces.Space:
return self._action_space
@property
def reward_space(self) -> gym.spaces.Space:
return self._reward_space
def __repr__(self) -> str:
return "DI-engine BSuite Env({})".format(self.env_id)
@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)]
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