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