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feat: add expectimax search
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Original file line number | Diff line number | Diff line change |
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import numpy as np | ||
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from gymnasium_2048.agents.ntuple.policy import NTupleNetworkBasePolicy | ||
from gymnasium_2048.envs import TwentyFortyEightEnv | ||
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class ExpectimaxSearch: | ||
def __init__( | ||
self, | ||
policy: NTupleNetworkBasePolicy, | ||
max_depth: int = 3, | ||
) -> None: | ||
self.policy = policy | ||
self.max_depth = max_depth | ||
self.min_value = 0.0 | ||
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def _evaluate(self, state: np.ndarray) -> tuple[float, int]: | ||
values = [ | ||
self.policy.evaluate(state=state, action=action) for action in range(4) | ||
] | ||
max_action = np.argmax(values) | ||
return max(self.min_value, values[max_action]), max_action | ||
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def _maximize(self, state: np.ndarray, depth: int) -> tuple[float, int]: | ||
if depth >= self.max_depth: | ||
return self._evaluate(state=state) | ||
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max_value = self.min_value | ||
max_action = 0 | ||
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for action in range(4): | ||
after_state, _, is_legal = TwentyFortyEightEnv.apply_action( | ||
board=state, | ||
action=action, | ||
) | ||
if not is_legal: | ||
continue | ||
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value = self._chance(after_state=after_state, depth=depth + 1) | ||
if value > max_value: | ||
max_value = value | ||
max_action = action | ||
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return max_value, max_action | ||
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def _chance(self, after_state: np.ndarray, depth: int) -> float: | ||
if depth >= self.max_depth: | ||
return self._evaluate(state=after_state)[0] | ||
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values, weights = [], [] | ||
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for row in range(after_state.shape[0]): | ||
for col in range(after_state.shape[1]): | ||
if after_state[row, col] != 0: | ||
continue | ||
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for value, prob in ((1, 0.9), (2, 0.1)): | ||
after_state[row, col] = value | ||
values.append(self._maximize(state=after_state, depth=depth + 1)[0]) | ||
weights.append(prob) | ||
after_state[row, col] = 0 | ||
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return np.average(values, weights=weights) | ||
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def predict(self, state: np.ndarray) -> int: | ||
value, action = self._maximize(state=state, depth=0) | ||
print(value, action) | ||
return action |