def startGame(self, trainingGame=False, learningRate=0.1, decay=0.99):
cross = 1
circle = -1
playerMoves = []
playerBoard = []
neuralMoves = []
neuralBoard = []
mark = circle
game = TicTacToe()
entry = ""
while entry != "exit" and game.finished() == False:
if entry == "print":
self.print()
else:
game.print()
if mark == cross:
mark = circle
board = game.getBoard(mark)
coord = self.action(board)
x = coord[0][0]
y = coord[0][1]
#print(coord)
print("x: " + str(x) + ", y: " + str(y))
while game.addMark(mark, x, y) == False:
if trainingGame == False:
break
out = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
value = self.coordinatesToIndex(x, y)
out[value] = 0.0
self.NN.trainWithOutput(learningRate, out)
coord = self.action(board)
x = coord[0][0]
y = coord[0][1]
if trainingGame:
value = self.coordinatesToIndex(x, y)
neuralMoves.append(value)
neuralBoard.append(board)
else:
mark = cross
entry = input()
if entry != "exit":
split = entry.split(',')
x = int(split[0])
y = int(split[1])
if game.addMark(mark, x, y) and trainingGame:
board = game.getBoard(mark)
value = self.coordinatesToIndex(x, y)
playerMoves.append(value)
playerBoard.append(board)
print()
if game.finished() == True:
game.print()
winner = game.getWinner()
print("Winner: " + str(winner))
if trainingGame:
neuralMoves.reverse()
neuralBoard.reverse()
if winner == cross:
for i in range(len(neuralMoves)):
target = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
target[neuralMoves[i]] = 0.0
self.NN.train(learningRate * (decay**i),
neuralBoard[i], target)
for i in range(len(playerMoves)):
target = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
target[playerMoves[i]] = 1.0
self.NN.train(learningRate * (decay**i),
playerBoard[i], target)
elif winner == circle:
for i in range(len(neuralMoves)):
target = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
target[neuralMoves[i]] = 1.0
self.NN.train(learningRate * (decay**i),
neuralBoard[i], target)
for i in range(len(playerMoves)):
target = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
target[playerMoves[i]] = 0.0
self.NN.train(learningRate * (decay**i),
playerBoard[i], target)
def trainFor(self, learningRate, decay, exploreRate, episodes):
cross = 1
circle = -1
gameNum = 0
for _ in range(episodes):
self.totalGamesTrained += 1
gameNum += 1
print("Number " + str(gameNum))
game = TicTacToe()
crossMoves = []
crossBoard = []
circleMoves = []
circleBoard = []
mark = circle
while game.finished() == False:
#game.print()
if mark == cross:
mark = circle
else:
mark = cross
x = None
y = None
board = game.getBoard(mark)
if random.random() >= exploreRate:
coord = self.action(board)
x = coord[0][0]
y = coord[0][1]
while game.addMark(mark, x, y) == False:
#game.print()
out = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
value = self.coordinatesToIndex(x, y)
out[value] = 0.0
self.NN.trainWithOutput(learningRate, out)
coord = self.action(board)
x = coord[0][0]
y = coord[0][1]
else:
pos = [[0, 0], [1, 0], [2, 0], [0, 1], [1, 1], [2, 1],
[0, 2], [1, 2], [2, 2]]
picked = random.choice(pos)
x = picked[0]
y = picked[1]
#print("Explored!")
while game.addMark(mark, x, y) == False:
pos.remove(picked)
picked = random.choice(pos)
x = picked[0]
y = picked[1]
value = self.coordinatesToIndex(x, y)
if mark == cross:
crossMoves.append(value)
crossBoard.append(board)
else:
circleMoves.append(value)
circleBoard.append(board)
#game.print()
crossMoves.reverse()
crossBoard.reverse()
circleMoves.reverse()
circleBoard.reverse()
if game.getWinner() == cross:
for i in range(len(crossMoves)):
target = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
target[crossMoves[i]] = 1.0
self.NN.train(learningRate * (decay**i), crossBoard[i],
target)
for i in range(len(circleMoves)):
target = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
target[circleMoves[i]] = 0.0
self.NN.train(learningRate * (decay**i), circleBoard[i],
target)
elif game.getWinner == circle:
for i in range(len(circleMoves)):
target = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
target[circleMoves[i]] = 1.0
self.NN.train(learningRate * (decay**i), circleBoard[i],
target)
for i in range(len(crossMoves)):
target = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
target[crossMoves[i]] = 0.0
self.NN.train(learningRate * (decay**i), crossBoard[i],
target)
