我正在使用 Python 为游戏 2048 编写 AI。它比我预期的要慢得多。我将深度限制设置为 5,但仍然需要几秒钟才能得到答案。起初我以为我所有功能的实现都是垃圾,但我想出了真正的原因。搜索树上的叶子比应该有的多得多。
这是一个典型的结果(我数了树叶、树枝和展开的数量):
111640 leaves, 543296 branches, 120936 expansions
Branching factor: 4.49242574585
Expected max leaves = 4.49242574585^5 = 1829.80385192 leaves
还有一个,很好的衡量标准:
99072 leaves, 488876 branches, 107292 expansions
Branching factor: 4.55650001864
Expected max leaves = 4.55650001864^5 = 1964.06963743 leaves
如您所见,搜索树上的叶子比我使用朴素极小极大时的叶子要多得多。这里发生了什么?我的算法发布在下面:
# Generate constants
import sys
posInfinity = sys.float_info.max
negInfinity = -sys.float_info.max
# Returns the direction of the best move given current state and depth limit
def bestMove(grid, depthLimit):
global limit
limit = depthLimit
moveValues = {}
# Match each move to its minimax value
for move in Utils2048.validMoves(grid):
gridCopy = [row[:] for row in grid]
Utils2048.slide(gridCopy, move)
moveValues[move] = minValue(grid, negInfinity, posInfinity, 1)
# Return move that have maximum value
return max(moveValues, key = moveValues.get)
# Returns the maximum utility when the player moves
def maxValue(grid, a, b, depth):
successors = Utils2048.maxSuccessors(grid)
if len(successors) == 0 or limit < depth:
return Evaluator.evaluate(grid)
value = negInfinity
for successor in successors:
value = max(value, minValue(successor, a, b, depth + 1))
if value >= b:
return value
a = max(a, value)
return value
# Returns the minimum utility when the computer moves
def minValue(grid, a, b, depth):
successors = Utils2048.minSuccessors(grid)
if len(successors) == 0 or limit < depth:
return Evaluator.evaluate(grid)
value = posInfinity
for successor in successors:
value = min(value, maxValue(successor, a, b, depth + 1))
if value <= a:
return value
b = min(b, value)
return value
有人请帮帮我。我多次查看此代码,但无法确定问题所在。