我用α-β剪枝实现了一个极大极小算法。为了获得最佳移动,我使用rootAlphaBeta
函数调用alpha-beta算法。然而,在rootAlphaBeta
函数中,我发现了一些非常奇怪的行为。当我用4的ply
调用rootAlphaBeta
函数时,它会进行大约20000次调用,但是当我直接调用alphaBeta
函数时,它只会进行大约2000次调用。我好像找不到什么问题,因为电话号码应该是一样的。在
两种算法最终找到的移动应该是相同的,对吧?我想是的,至少这一步的分数是一样的,我不知道alphaBeta
在没有rootAlphaBeta
的情况下直接调用它所选择的移动。在
def alphaBeta(self, board, rules, alpha, beta, ply, player):
"""Implements a minimax algorithm with alpha-beta pruning."""
if ply == 0:
return self.positionEvaluation(board, rules, player)
move_list = board.generateMoves(rules, player)
for move in move_list:
board.makeMove(move, player)
current_eval = -self.alphaBeta(board, rules, -beta, -alpha, ply - 1,
board.getOtherPlayer(player))
board.unmakeMove(move, player)
if current_eval >= beta:
return beta
if current_eval > alpha:
alpha = current_eval
return alpha
def rootAlphaBeta(self, board, rules, ply, player):
"""Makes a call to the alphaBeta function. Returns the optimal move for a
player at given ply."""
best_move = None
max_eval = float('-infinity')
move_list = board.generateMoves(rules, player)
for move in move_list:
board.makeMove(move, player)
current_eval = -self.alphaBeta(board, rules, float('-infinity'),
float('infinity'), ply - 1,
board.getOtherPlayer(player))
board.unmakeMove(move, player)
if current_eval > max_eval:
max_eval = current_eval
best_move = move
return best_move
您的
rootAlphaBeta
不更新alpha
值。它用(-inf,inf)的完整范围调用它的所有子节点,而它本可以缩小除第一个子节点之外的所有子节点的范围。这将阻止对最终得分没有影响的某些分支的修剪,并增加节点数。在相关问题 更多 >
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