def test(net, epochs):
net.eval()
numWins = 0
numLosses = 0
numTies = 0
for i in tqdm(range(epochs)):
player = 2
computersPlayer = random.randint(1,2)
board = np.zeros(shape = (3, 3))
# board = np.random.randint(low = 0, high = 3, size = (3, 3))
movesLeft = np.any(np.where(board == 0, 1, 0))
winner = tt.getWinner(board)
while(not winner and movesLeft):
if player == computersPlayer:
# generate a move
oneHot = oneHotTicTacToe(board, computersPlayer).view(1, 1, 18)
output = net(oneHot)
# mask out invalid moves
invalidMoves = np.where( board.flatten() > 0, True, False)
maskedOutput = output.clone().view(9)
maskedOutput[invalidMoves] = -10
values, index = maskedOutput.max(0)
# apply the move
move = index
board = board.flatten()
board[move] = computersPlayer
board = board.reshape(3, 3)
else: # opponents turn
empties = tt.listEmpties(board)
randomMove = random.choice(empties)
tt.applyMove(player, randomMove, board)
player = tt.togglePlayer(player)
movesLeft = np.any(np.where(board == 0, 1, 0))
winner = tt.getWinner(board)
if winner == computersPlayer:
numWins += 1
elif winner == tt.togglePlayer(computersPlayer):
numLosses += 1
else: # winner == False
numTies += 1
return numWins, numLosses, numTies
def handler(x, y, g, p1, p2, b, eve):
"""
handles the click
:param x: x-coordinate
:param y: y-coordinate
:param g: situation
:param p1: player
:param p2: player
:param b: list of buttons
:param eve: the event
"""
global current
spec = Player.get_spec(current)
if g[x][y] == '':
g[x][y] = spec
if Player.get_name(current) != 'computer' and Player.get_name(
switch_player(
current, p1,
p2)) == "computer" and not tictactoe.isFinished(g):
g = minmax.IA(g, switch_player(current, p1, p2), current, 10,
tictactoe)
else:
current = switch_player(current, p1, p2)
__redraw(g, b)
if tictactoe.isFinished(g):
winner = tictactoe.getWinner(g, g, switch_player(current, p1, p2))
_disable(g, b)
msg = finalState(winner)
msgbox.showinfo("finished", msg)
def simulateChildren(mct, board, player, myPlayer, numSims, verbose=False):
key = genFullKey(board, player)
if key not in mct:
mct[key] = 0
nextBoards = tt.listNextBoards(board, player)
winner = tt.getWinner(board)
if not winner and nextBoards:
# add next boards to mct
if verbose:
for nextBoard in tqdm(nextBoards):
simChildrenInner(mct, nextBoard, player, myPlayer, numSims)
else:
for nextBoard in nextBoards:
simChildrenInner(mct, nextBoard, player, myPlayer, numSims)
# pick the highest score and return that
scores = [
mct[genFullKey(nextBoard, tt.togglePlayer(player))]
for nextBoard in nextBoards
]
highestScore = sorted(scores)[-1]
return highestScore
else:
score = scoreEndBoard(board, tt.togglePlayer(player), myPlayer)
score = score * math.pow(numSims, 2)
mct[key] = score
return score
def train(qTables, numGames, alpha, tryHard):
tryHardGrowth = (1 - tryHard) / numGames
for i in tqdm(range(numGames)):
board = tt.genBoard()
movesLeft = True
winner = False
player = 2
keysSoFar = []
movesSoFar = []
computersPlayer = random.randint(1, 2)
while (movesLeft and not winner):
if player == computersPlayer:
bestMove = pickBestNextMove(qTables, keysSoFar, board, player,
computersPlayer, tryHard)
movesSoFar.append(bestMove)
tt.applyMove(player, bestMove, board)
else:
moves = tt.listEmpties(board)
randomMove = random.choice(moves)
tt.applyMove(player, randomMove, board)
player = tt.togglePlayer(player)
winner = tt.getWinner(board)
movesLeft = not tt.noMoreMoves(board)
score = scoreEndBoard(board, winner, computersPlayer)
updateQTable(score, qTables, keysSoFar, movesSoFar, alpha)
tryHard = tryHard + tryHardGrowth
def minimaxGenBoardScores(inBoard, myPlayer, inPlayer):
stack = []
boardScores = {}
stack.append({'board': inBoard, 'player': inPlayer})
while len(stack) > 0:
args = stack[-1]
board = args['board']
player = args['player']
maximizing = False
if player == myPlayer:
maximizing = True
# base case, end board
winner = tt.getWinner(board)
if winner:
boardScores[tt.hash(board)] = tt.scoreEndBoard(
board, winner, myPlayer)
print("###########")
tt.printBoard(board)
print(boardScores[tt.hash(board)])
stack.pop()
elif tt.noMoreMoves(board):
boardScores[tt.hash(board)] = tt.scoreEndBoard(
board, winner, myPlayer)
stack.pop()
else: # nobody won yet, and there are move moves
nextBoards = tt.listNextBoards(board, player)
allPresent = True
for nextBoard in nextBoards:
if not (tt.hash(nextBoard) in boardScores):
allPresent = False
newArgs = {
'board': nextBoard,
'player': tt.togglePlayer(player),
}
stack.append(newArgs)
if allPresent:
scores = [
boardScores[tt.hash(nextBoard)] for board in nextBoards
]
if maximizing:
boardScores[tt.hash(board)] = max(scores)
else:
boardScores[tt.hash(board)] = min(scores)
stack.pop()
return boardScores
def minimax_inner(count, board, player, myPlayer, prevBoards, alpha, beta):
hashKey = tt.hash(board)
if hashKey in prevBoards:
return prevBoards[hashKey]
count[0] += 1
winner = tt.getWinner(board)
if winner:
score = scoreEndBoard(board, winner, myPlayer)
# prevBoards[hashKey] = score
return score
elif tt.noMoreMoves(board):
score = scoreEndBoard(board, winner, myPlayer)
# prevBoards[hashKey] = score
return score
else:
nextBoards = tt.listNextBoards(board, tt.togglePlayer(player))
if player == myPlayer: # maximizing next moves
bestScore = -math.inf
for nextBoard in nextBoards:
if beta <= alpha:
break
score = minimax_inner(count,
nextBoard,
player=tt.togglePlayer(player),
myPlayer=myPlayer,
prevBoards=prevBoards,
alpha=alpha,
beta=beta)
prevBoards[tt.hash(nextBoard)] = score
if score > bestScore:
bestScore = score
alpha = bestScore
return bestScore
else: # minimizing next moves
bestScore = math.inf
for nextBoard in nextBoards:
if beta <= alpha:
break
score = minimax_inner(count,
nextBoard,
player=tt.togglePlayer(player),
myPlayer=myPlayer,
prevBoards=prevBoards,
alpha=alpha,
beta=beta)
prevBoards[tt.hash(nextBoard)] = score
if score < bestScore:
bestScore = score
beta = bestScore
return bestScore
def simulate(numSimulations, board, player, myPlayer):
originBoard = copy.deepcopy(board)
originPlayer = player
totalScore = 0
for i in range(numSimulations):
simBoard = copy.deepcopy(originBoard)
simPlayer = originPlayer
winner = tt.getWinner(simBoard)
movesLeft = not tt.noMoreMoves(simBoard)
while (movesLeft and not winner):
moves = tt.listEmpties(simBoard)
randomMove = random.choice(moves)
tt.applyMove(simPlayer, randomMove, simBoard)
simPlayer = tt.togglePlayer(simPlayer)
winner = tt.getWinner(simBoard)
movesLeft = not tt.noMoreMoves(simBoard)
score = scoreEndBoard(simBoard, winner, myPlayer)
totalScore += score
return totalScore
def playGame():
board = tt.genBoard()
movesLeft = True
winner = False
player = 2
computersPlayer = 2 #random.randint(1,2)
turn = 0
print("NEW GAME")
if computersPlayer == 2:
print("COMPUTER GOES FIRST...")
while (movesLeft and not winner):
if player == 2:
print("X's Turn")
else: # player == 1
print("O's Turn")
tt.printBoard(board)
if player == computersPlayer:
board = pickBestNextBoard(board, player, computersPlayer)
player = tt.togglePlayer(player)
elif player == tt.togglePlayer(computersPlayer):
validMove = False
while validMove == False:
move = input("input move of form 'y x' ")
y = int(move[0])
x = int(move[2])
# validate move
if board[y][x] is not 0:
print("!!!INVALID MOVE!!!")
continue
else:
validMove = True
board[y][x] = tt.togglePlayer(computersPlayer)
player = tt.togglePlayer(player)
turn += 1
winner = tt.getWinner(board)
movesLeft = not tt.noMoreMoves(board)
tt.printBoard(board)
if winner:
if winner == 2:
print("WINNER: X")
else: # winner == 1
print("WINNER: O")
else:
print("TIE")
def test(mct, numGames, numSims):
numWins = 0
numTies = 0
numLosses = 0
for i in tqdm(range(numGames)):
board = tt.genBoard()
movesLeft = True
winner = False
player = 2
computersPlayer = random.randint(1, 2)
while (movesLeft and not winner):
if player == computersPlayer:
simulateChildren(mct, board, player, computersPlayer, numSims)
bestBoard = pickBestNextMove(mct, board, player)
# print("################")
# tt.printBoard(board)
# tt.printBoard(bestBoard)
# print("BESTMOVE")
board = bestBoard
else:
moves = tt.listEmpties(board)
randomMove = random.choice(moves)
tt.applyMove(player, randomMove, board)
player = tt.togglePlayer(player)
winner = tt.getWinner(board)
movesLeft = not tt.noMoreMoves(board)
if winner == computersPlayer:
numWins += 1
elif winner == tt.togglePlayer(computersPlayer):
numLosses += 1
else: # tie
numTies += 1
return numWins, numLosses, numTies
def test(qTables, numGames, tryHard=1.0):
numWins = 0
numTies = 0
numLosses = 0
for i in tqdm(range(numGames)):
board = tt.genBoard()
movesLeft = True
winner = False
player = 2
keysSoFar = []
movesSoFar = []
computersPlayer = random.randint(1, 2)
while (movesLeft and not winner):
if player == computersPlayer:
bestMove = pickBestNextMove(qTables, keysSoFar, board, player,
computersPlayer, tryHard)
movesSoFar.append(bestMove)
tt.applyMove(player, bestMove, board)
else:
moves = tt.listEmpties(board)
randomMove = random.choice(moves)
tt.applyMove(player, randomMove, board)
player = tt.togglePlayer(player)
winner = tt.getWinner(board)
movesLeft = not tt.noMoreMoves(board)
if winner == computersPlayer:
numWins += 1
elif winner == tt.togglePlayer(computersPlayer):
numLosses += 1
else: # tie
numTies += 1
return numWins, numLosses, numTies
def playGame():
saveQTables = False
fileName = 'qTables.pickle'
qTables = {}
keysSoFar = []
movesSoFar = []
tryHard = 0
alpha = 0.9
numTrials = 1000000
if saveQTables:
train(qTables, numTrials, alpha, tryHard)
f = open(fileName, 'wb')
pickle.dump(qTables, f, pickle.HIGHEST_PROTOCOL)
f.close()
else:
f = open(fileName, 'rb')
qTables = pickle.load(f)
f.close()
numWins, numLosses, numTies = test(qTables, 1000)
print("VS RANDOM OPPONENT...")
print("numWins:" + str(numWins))
print("numLosses:" + str(numLosses))
print("numTies:" + str(numTies))
quit()
board = tt.genBoard()
movesLeft = True
winner = False
player = 2
computersPlayer = random.randint(1, 2)
print("NEW GAME")
if computersPlayer == 2:
print("COMPUTER GOES FIRST...")
while (movesLeft and not winner):
if player == 2:
print("X's Turn")
else: # player == 1
print("O's Turn")
tt.printBoard(board)
if player == computersPlayer:
bestMove = pickBestNextMove(qTables,
keysSoFar,
board,
player,
computersPlayer,
tryHard=1.0,
verbose=True)
movesSoFar.append(bestMove)
tt.applyMove(player, bestMove, board)
player = tt.togglePlayer(player)
elif player == tt.togglePlayer(computersPlayer):
validMove = False
while validMove == False:
move = input("input move of form 'y x' ")
y = int(move[0])
x = int(move[2])
# validate move
if board[y][x] is not 0:
print("!!!INVALID MOVE!!!")
continue
else:
validMove = True
board[y][x] = tt.togglePlayer(computersPlayer)
player = tt.togglePlayer(player)
winner = tt.getWinner(board)
movesLeft = not tt.noMoreMoves(board)
tt.printBoard(board)
score = scoreEndBoard(board, winner, computersPlayer)
updateQTable(score, qTables, keysSoFar, movesSoFar, alpha)
for key in keysSoFar:
pprint(key)
pprint(qTables[key])
if winner:
if winner == 2:
print("WINNER: X")
else: # winner == 1
print("WINNER: O")
else:
print("TIE")
def test(net, criterion, optimizer, epochs):
numInvalidMoves = 0
numWins = 0
numLosses = 0
numTies = 0
optimizer.zero_grad()
for i in tqdm(range(epochs)):
player = 2
computersPlayer = random.randint(1,2)
board = np.zeros(shape = (3, 3))
# board = np.random.randint(low = 0, high = 3, size = (3, 3))
movesLeft = np.any(np.where(board == 0, 1, 0))
winner = tt.getWinner(board)
while(not winner and movesLeft):
if player == computersPlayer:
move = None
moveValid = False
while not moveValid:
# generate a move
oneHot = oneHotTicTacToe(board, computersPlayer).view(1, 1, 18)
output = net(oneHot)
values, index = output.view(9).max(0)
if board.flatten()[index] == 0: # if move is valid
moveValid = True
# apply the move
move = index
board = board.flatten()
board[move] = computersPlayer
board = board.reshape(3, 3)
else: # invalid move, prime the whip
# print("invalid move")
numInvalidMoves += 1
optimizer.zero_grad()
validMoves = np.where(board == 0, 1, 0)
target = torch.tensor(validMoves, dtype=torch.float).view(1, 1, 9)
loss = criterion(output, target)
loss.backward()
optimizer.step()
else: # opponents turn
empties = tt.listEmpties(board)
randomMove = random.choice(empties)
tt.applyMove(player, randomMove, board)
player = tt.togglePlayer(player)
movesLeft = np.any(np.where(board == 0, 1, 0))
winner = tt.getWinner(board)
if winner == computersPlayer:
numWins += 1
elif winner == tt.togglePlayer(computersPlayer):
numLosses += 1
else: # winner == False
numTies += 1
return numWins, numLosses, numTies
def playGame():
mct = {}
board = tt.genBoard()
player = 2
computersPlayer = 2
numSimsPreGame = 100000
numSimsOnline = 100
saveMCTree = False
fileName = 'mct.pickle'
if saveMCTree:
simulateChildren(mct,
board,
player,
computersPlayer,
numSimsPreGame,
verbose=True)
f = open(fileName, 'wb')
pickle.dump(mct, f, pickle.HIGHEST_PROTOCOL)
f.close()
quit()
else:
f = open(fileName, 'rb')
mct = pickle.load(f)
f.close()
# mct = {}
# numTrials = 100
# numWins, numLosses, numTies = test(mct, numTrials, numSimsOnline)
# print("VS RANDOM OPPONENT...")
# print("numWins:" + str(numWins))
# print("numLosses:" + str(numLosses))
# print("numTies:" + str(numTies))
# quit()
# w 0.6, t 0.11, l 0.3
board = tt.genBoard()
movesLeft = True
winner = False
player = 2
computersPlayer = random.randint(1, 2)
print("NEW GAME")
if computersPlayer == 2:
print("COMPUTER GOES FIRST...")
while (movesLeft and not winner):
if player == 2:
print("X's Turn")
else: # player == 1
print("O's Turn")
tt.printBoard(board)
if player == computersPlayer:
simulateChildren(mct,
board,
player,
computersPlayer,
numSimsOnline,
verbose=True)
bestBoard = pickBestNextMove(mct, board, player)
board = bestBoard
elif player == tt.togglePlayer(computersPlayer):
validMove = False
while validMove == False:
move = input("input move of form 'y x' ")
y = int(move[0])
x = int(move[2])
# validate move
if board[y][x] is not 0:
print("!!!INVALID MOVE!!!")
continue
else:
validMove = True
board[y][x] = tt.togglePlayer(computersPlayer)
player = tt.togglePlayer(player)
winner = tt.getWinner(board)
movesLeft = not tt.noMoreMoves(board)
tt.printBoard(board)
score = scoreEndBoard(board, winner, computersPlayer)
if winner:
if winner == 2:
print("WINNER: X")
else: # winner == 1
print("WINNER: O")
else:
print("TIE")
def train(net, criterion, optimizer, epochs):
numInvalidMoves = 0
for i in tqdm(range(epochs)):
player = 2
computersPlayer = random.randint(1,2)
optimizer.zero_grad()
board = np.zeros(shape = (3, 3))
# board = np.random.randint(low = 0, high = 3, size = (3, 3))
movesLeft = np.any(np.where(board == 0, 1, 0))
winner = tt.getWinner(board)
moves = []
outputs = []
while(not winner and movesLeft):
if player == computersPlayer:
move = None
moveValid = False
while not moveValid:
# generate a move
oneHot = oneHotTicTacToe(board, computersPlayer).view(1, 1, 18)
output = net(oneHot)
values, index = output.view(9).max(0)
if board.flatten()[index] == 0: # if move is valid
moveValid = True
# apply the move
move = index
board = board.flatten()
board[move] = computersPlayer
board = board.reshape(3, 3)
# store for later
moves.append(move)
outputs.append(output)
else: # invalid move, prime the whip
# print("invalid move")
numInvalidMoves += 1
optimizer.zero_grad()
validMoves = np.where(board == 0, 1, 0)
target = torch.tensor(validMoves, dtype=torch.float).view(1, 1, 9)
loss = criterion(output, target)
loss.backward()
optimizer.step()
else: # opponents turn
empties = tt.listEmpties(board)
randomMove = random.choice(empties)
tt.applyMove(player, randomMove, board)
player = tt.togglePlayer(player)
movesLeft = np.any(np.where(board == 0, 1, 0))
winner = tt.getWinner(board)
# get end score of game
score = scoreEndBoard(board, winner, computersPlayer)
for i, move in enumerate(moves):
output = outputs[i]
target = output.clone().view(9)
target[move] = score
target = target.view(1, 1, 9)
optimizer.zero_grad()
loss = criterion(output, target)
loss.backward()
optimizer.step()
bestMove = randomMove
else:
bestMoveDict = pickBestMove(nextMoves, player, computersPlayer)
bestMove = bestMoveDict['move']
score = bestMoveDict['score']
trainingSession = bestMoveDict['trainingSession']
if player == 1:
playerOneTrainingSessions.append(trainingSession)
else: # player == 2
playerTwoTrainingSessions.append(trainingSession)
board = bestMove
player = tt.togglePlayer(player)
winner = tt.getWinner(board)
movesLeft = not tt.noMoreMoves(board)
# do backprop here
playerOneTruth = None
playerTwoTruth = None
if winner == False:
truth = np.array([0.0, 1.0, 0.0])
playerOneTruth = truth
playerTwoTruth = truth
elif winner == 1:
playerOneTruth = np.array([0.0, 0.0, 1.0])
playerTwoTruth = np.array([1.0, 0.0, 0.0])
else: # winner == 2
playerTwoTruth = np.array([0.0, 0.0, 1.0])
playerOneTruth = np.array([1.0, 0.0, 0.0])
def trainAgainstSelf(net, criterion, optimizer, epochs):
net.train()
for i in tqdm(range(epochs)):
player = 2
computersPlayer = random.randint(1,2)
optimizer.zero_grad()
board = np.zeros(shape = (3, 3))
# board = np.random.randint(low = 0, high = 3, size = (3, 3))
movesLeft = np.any(np.where(board == 0, 1, 0))
winner = tt.getWinner(board)
movesA = []
outputsA = []
movesB = []
outputsB = []
while(not winner and movesLeft):
# generate a move
if player == computersPlayer:
oneHot = oneHotTicTacToe(board, computersPlayer).view(1, 1, 18)
else:
oneHot = oneHotTicTacToe(board, tt.togglePlayer(computersPlayer)).view(1, 1, 18)
output = net(oneHot)
# mask out invalid moves
invalidMoves = np.where( board.flatten() > 0, True, False)
maskedOutput = output.clone().view(9)
maskedOutput[invalidMoves] = -10
values, index = maskedOutput.max(0)
# apply the move
move = index
board = board.flatten()
if player == computersPlayer:
board[move] = computersPlayer
else:
board[move] = tt.togglePlayer(computersPlayer)
board = board.reshape(3, 3)
# store for later
if player == computersPlayer:
movesA.append(move)
outputsA.append(output)
else:
movesB.append(move)
outputsB.append(output)
player = tt.togglePlayer(player)
movesLeft = np.any(np.where(board == 0, 1, 0))
winner = tt.getWinner(board)
# get end score of game
score = scoreEndBoard(board, winner, computersPlayer)
for i, move in enumerate(movesA):
output = outputsA[i]
target = output.clone().view(9)
target[move] = score
target = target.view(1, 1, 9)
optimizer.zero_grad()
loss = criterion(output, target)
loss.backward()
optimizer.step()
score = scoreEndBoard(board, winner, tt.togglePlayer(computersPlayer))
for i, move in enumerate(movesB):
output = outputsB[i]
target = output.clone().view(9)
target[move] = score
target = target.view(1, 1, 9)
optimizer.zero_grad()
loss = criterion(output, target)
loss.backward()
optimizer.step()
def playWithUser(net, online=True):
while(True):
board = genBoard()
movesLeft = True
winner = False
player = 2
computersPlayer = 2 #random.randint(1,2)
print()
print("NEW GAME")
if computersPlayer == 2:
print("COMPUTER GOES FIRST...")
moves = []
outputs = []
while(movesLeft and not winner):
if player == 2:
print("X's Turn")
else: # player == 1
print("O's Turn")
tt.printBoard(board)
if player == computersPlayer:
move = None
moveValid = False
while not moveValid:
# generate a move
oneHot = oneHotTicTacToe(board, computersPlayer).view(1, 1, 18)
output = net(oneHot)
values, index = output.view(9).max(0)
if board.flatten()[index] == 0: # if move is valid
moveValid = True
# apply the move
move = index
board = board.flatten()
board[move] = computersPlayer
board = board.reshape(3, 3)
# store for later
moves.append(move)
outputs.append(output)
else: # invalid move, prime the whip
print("invalid move")
optimizer.zero_grad()
validMoves = np.where(board == 0, 1, 0)
target = torch.tensor(validMoves, dtype=torch.float).view(1, 1, 9)
loss = criterion(output, target)
loss.backward()
optimizer.step()
elif player == tt.togglePlayer(computersPlayer):
validMove = False
while validMove == False:
move = input("input move of form 'y x' ")
y = int(move[0])
x = int(move[2])
# validate move
if not board[y][x] == 0:
print("!!!INVALID MOVE!!!")
continue
else:
validMove = True
board[y][x] = tt.togglePlayer(computersPlayer)
player = tt.togglePlayer(player)
winner = tt.getWinner(board)
movesLeft = not tt.noMoreMoves(board)
tt.printBoard(board)
if online:
score = scoreEndBoard(board, winner, computersPlayer)
for i, move in enumerate(moves):
output = outputs[i]
target = output.clone().view(9)
target[move] = score
target = target.view(1, 1, 9)
optimizer.zero_grad()
loss = criterion(output, target)
loss.backward()
optimizer.step()
if winner:
if winner == 2:
print("WINNER: X")
else: # winner == 1
print("WINNER: O")
else:
print("TIE")
def train(net, criterion, optimizer, epochs):
net.train()
for i in tqdm(range(epochs)):
player = 2
computersPlayer = random.randint(1,2)
optimizer.zero_grad()
board = np.zeros(shape = (3, 3))
movesLeft = np.any(np.where(board == 0, 1, 0))
winner = tt.getWinner(board)
gameDuration = 0
moves = []
outputs = []
while(not winner and movesLeft):
if player == computersPlayer:
# generate a move
oneHot = oneHotTicTacToe(board, computersPlayer).view(1, 1, 18)
output = net(oneHot)
# mask out invalid moves
invalidMoves = np.where( board.flatten() > 0, True, False)
maskedOutput = output.clone().view(9)
maskedOutput[invalidMoves] = -10
values, index = maskedOutput.max(0)
# apply the move
move = index
board = board.flatten()
board[move] = computersPlayer
board = board.reshape(3, 3)
# store for later
moves.append(move)
outputs.append(output)
else: # opponents turn
empties = tt.listEmpties(board)
randomMove = random.choice(empties)
tt.applyMove(player, randomMove, board)
player = tt.togglePlayer(player)
gameDuration += 1
movesLeft = np.any(np.where(board == 0, 1, 0))
winner = tt.getWinner(board)
# get end score of game
score = scoreEndBoard(board, winner, computersPlayer)
# gameDurationMultiplier = 1.0 - gameDuration / 10
# gameDurationMultiplier = gameDurationMultiplier * 0.9
dilutionFactor = 0.9
totalDilutant = 1.0
for i, move in reversed(list(enumerate(moves))):
totalDilutant *= dilutionFactor
output = outputs[i]
target = output.clone().view(9)
target[move] = score * totalDilutant
target = target.view(1, 1, 9)
optimizer.zero_grad()
loss = criterion(output, target)
loss.backward()
optimizer.step()