def _alpha_beta_search(game: MancalaEnv, alpha=-np.inf, beta=np.inf, depth=5):
"""Search game to determine best action; use alpha-beta pruning.
This version cuts off search and uses an evaluation function."""
if depth == 0 or game.is_game_over():
return game.get_player_utility()
if game.side_to_move == Side.SOUTH:
v = -np.inf
for (_, new_s) in game.next_states():
v = max(v, AlphaBeta._alpha_beta_search(new_s, alpha, beta, depth - 1))
alpha = max(alpha, v)
# if beta <= alpha:
# break
else:
v = np.inf
for (_, new_s) in game.next_states():
v = min(v, AlphaBeta._alpha_beta_search(new_s, alpha, beta, depth - 1))
beta = min(beta, v)
# if beta <= alpha:
# break
return v
def search(self, game: MancalaEnv) -> Move:
values = [(a, self._alpha_beta_search(game=state, depth=self.depth)) for a, state in game.next_states()]
np.random.shuffle(values)
if game.side_to_move == Side.SOUTH:
action, _ = max(values, key=lambda x: x[1])
else:
action, _ = min(values, key=lambda x: x[1])
return action