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| from typing import List | |
| import numpy as np | |
| import gym | |
| from ding.envs import BaseEnv, BaseEnvTimestep | |
| class GameEnv(BaseEnv): | |
| def __init__(self, game_type: str = 'prisoner_dilemma') -> None: | |
| self.game_type = game_type | |
| assert self.game_type in ['zero_sum', 'prisoner_dilemma'] | |
| if self.game_type == 'prisoner_dilemma': | |
| self.optimal_policy = [0, 1] | |
| elif self.game_type == 'zero_sum': | |
| self.optimal_policy = [0.375, 0.625] | |
| self._observation_space = None | |
| self._action_space = None | |
| self._reward_space = None | |
| def seed(self, seed: int, dynamic_seed: bool = False) -> None: | |
| # ignore seed | |
| pass | |
| def reset(self) -> np.ndarray: | |
| return np.array([[0, 1], [1, 0]]).astype(np.float32) # trivial observation | |
| def step(self, actions: List[int]) -> BaseEnvTimestep: | |
| if self.game_type == 'zero_sum': | |
| if actions == [0, 0]: | |
| rewards = 3, -3 | |
| results = "wins", "losses" | |
| elif actions == [0, 1]: | |
| rewards = -2, 2 | |
| results = "losses", "wins" | |
| elif actions == [1, 0]: | |
| rewards = -2, 2 | |
| results = "losses", "wins" | |
| elif actions == [1, 1]: | |
| rewards = 1, -1 | |
| results = "wins", "losses" | |
| else: | |
| raise RuntimeError("invalid actions: {}".format(actions)) | |
| elif self.game_type == 'prisoner_dilemma': | |
| if actions == [0, 0]: | |
| rewards = -1, -1 | |
| results = "draws", "draws" | |
| elif actions == [0, 1]: | |
| rewards = -20, 0 | |
| results = "losses", "wins" | |
| elif actions == [1, 0]: | |
| rewards = 0, -20 | |
| results = "wins", "losses" | |
| elif actions == [1, 1]: | |
| rewards = -10, -10 | |
| results = 'draws', 'draws' | |
| else: | |
| raise RuntimeError("invalid actions: {}".format(actions)) | |
| observations = np.array([[0, 1], [1, 0]]).astype(np.float32) | |
| rewards = np.array(rewards).astype(np.float32) | |
| rewards = rewards[..., np.newaxis] | |
| dones = True, True | |
| infos = { | |
| 'result': results[0], | |
| 'eval_episode_return': rewards[0] | |
| }, { | |
| 'result': results[1], | |
| 'eval_episode_return': rewards[1] | |
| } | |
| return BaseEnvTimestep(observations, rewards, True, infos) | |
| def close(self) -> None: | |
| pass | |
| def __repr__(self) -> str: | |
| return "DI-engine League Demo GameEnv" | |
| 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 random_action(self) -> List[int]: | |
| return [np.random.randint(0, 2) for _ in range(2)] | |