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| import numpy as np | |
| def pfsp(win_rates: np.ndarray, weighting: str) -> np.ndarray: | |
| """ | |
| Overview: | |
| Prioritized Fictitious Self-Play algorithm. | |
| Process win_rates with a weighting function to get priority, then calculate the selection probability of each. | |
| Arguments: | |
| - win_rates (:obj:`np.ndarray`): a numpy ndarray of win rates between one player and N opponents, shape(N) | |
| - weighting (:obj:`str`): pfsp weighting function type, refer to ``weighting_func`` below | |
| Returns: | |
| - probs (:obj:`np.ndarray`): a numpy ndarray of probability at which one element is selected, shape(N) | |
| """ | |
| weighting_func = { | |
| 'squared': lambda x: (1 - x) ** 2, | |
| 'variance': lambda x: x * (1 - x), | |
| } | |
| if weighting in weighting_func.keys(): | |
| fn = weighting_func[weighting] | |
| else: | |
| raise KeyError("invalid weighting arg: {} in pfsp".format(weighting)) | |
| assert isinstance(win_rates, np.ndarray) | |
| assert win_rates.shape[0] >= 1, win_rates.shape | |
| # all zero win rates case, return uniform selection prob | |
| if win_rates.sum() < 1e-8: | |
| return np.full_like(win_rates, 1.0 / len(win_rates)) | |
| fn_win_rates = fn(win_rates) | |
| probs = fn_win_rates / fn_win_rates.sum() | |
| return probs | |
| def uniform(win_rates: np.ndarray) -> np.ndarray: | |
| """ | |
| Overview: | |
| Uniform opponent selection algorithm. Select an opponent uniformly, regardless of historical win rates. | |
| Arguments: | |
| - win_rates (:obj:`np.ndarray`): a numpy ndarray of win rates between one player and N opponents, shape(N) | |
| Returns: | |
| - probs (:obj:`np.ndarray`): a numpy ndarray of uniform probability, shape(N) | |
| """ | |
| return np.full_like(win_rates, 1.0 / len(win_rates)) | |