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🤖 Alpha-Beta pruning

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NikolajDanger
2020-08-02 00:12:50 +02:00
parent 00fbb2b06d
commit 4b6bccf909
1 changed files with 15 additions and 7 deletions
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22
funcs/games/fourInARow.py
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@ -7,10 +7,10 @@ from . import fourInARowDraw
from funcs import logThis
AIScores = {
"middle": 1,
"two in a row": 5,
"middle": 3,
"two in a row": 10,
"three in a row": 50,
"enemy two in a row": -25,
"enemy two in a row": -35,
"enemy three in a row": -200,
"enemy win": -10000,
"win": 420,
@ -222,7 +222,7 @@ def fourInARowAI(channel):
testBoard = copy.deepcopy(board)
testBoard = placeOnBoard(testBoard,player,column)
if testBoard != None:
scores[column] = minimax(testBoard,4,player%2+1,player,False)
scores[column] = minimax(testBoard,5,player%2+1,player,-math.inf,math.inf,False)
logThis("Best score for column "+str(column)+" is "+str(scores[column]))
possibleScores = scores.copy()
@ -295,7 +295,7 @@ def evaluateWindow(window,player,otherPlayer):
else:
return 0
def minimax(board, depth, player , originalPlayer, maximizingPlayer):
def minimax(board, depth, player , originalPlayer, alpha, beta, maximizingPlayer):
terminal = ((isWon(board)[0] != 0) or (0 not in board[0]))
if depth == 0 or terminal:
points = AICalcPoints(board,originalPlayer)
@ -306,7 +306,11 @@ def minimax(board, depth, player , originalPlayer, maximizingPlayer):
testBoard = copy.deepcopy(board)
testBoard = placeOnBoard(testBoard,player,column)
if testBoard != None:
value = max(value,minimax(testBoard,depth-1,player%2+1,originalPlayer,False))
evaluation = minimax(testBoard,depth-1,player%2+1,originalPlayer,alpha,beta,False)
value = max(value,evaluation)
alpha = max(alpha,evaluation)
if beta <= alpha:
break
return value
else:
value = math.inf
@ -314,6 +318,10 @@ def minimax(board, depth, player , originalPlayer, maximizingPlayer):
testBoard = copy.deepcopy(board)
testBoard = placeOnBoard(testBoard,player,column)
if testBoard != None:
value = min(value,minimax(testBoard,depth-1,player%2+1,originalPlayer,True))
evaluation = minimax(testBoard,depth-1,player%2+1,originalPlayer,alpha,beta,True)
value = min(value,evaluation)
beta = min(beta,evaluation)
if beta <= alpha:
break
return value