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import numpy as np
from dizoo.beergame.envs.beergame_core import BeerGame
from typing import Union, List, Optional
from ding.envs import BaseEnv, BaseEnvTimestep
from ding.utils import ENV_REGISTRY
from ding.torch_utils import to_ndarray
import copy
@ENV_REGISTRY.register('beergame')
class BeerGameEnv(BaseEnv):
def __init__(self, cfg: dict) -> None:
self._cfg = cfg
self._init_flag = False
def reset(self) -> np.ndarray:
if not self._init_flag:
self._env = BeerGame(self._cfg.role, self._cfg.agent_type, self._cfg.demandDistribution)
self._observation_space = self._env.observation_space
self._action_space = self._env.action_space
self._reward_space = self._env.reward_space
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()
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: Union[int, np.ndarray]) -> BaseEnvTimestep:
if isinstance(action, np.ndarray) and 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).astype(np.float32)
rew = to_ndarray([rew]).astype(np.float32) # wrapped to be transfered to a array with shape (1,)
return BaseEnvTimestep(obs, rew, done, info)
def reward_shaping(self, transitions: List[dict]) -> List[dict]:
new_transitions = copy.deepcopy(transitions)
for trans in new_transitions:
trans['reward'] = self._env.reward_shaping(trans['reward'])
return new_transitions
def random_action(self) -> np.ndarray:
random_action = self.action_space.sample()
if isinstance(random_action, int):
random_action = to_ndarray([random_action], dtype=np.int64)
return random_action
def enable_save_figure(self, figure_path: Optional[str] = None) -> None:
self._env.enable_save_figure(figure_path)
@property
def observation_space(self) -> int:
return self._observation_space
@property
def action_space(self) -> int:
return self._action_space
@property
def reward_space(self) -> int:
return self._reward_space
def __repr__(self) -> str:
return "DI-engine Beergame Env"
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