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Q:
ValueError in a Q-Learning Neural Network
I have just started with using neural networks and I have a problem I can't seem to solve. I want to implement a Q-learning neural network that learns how to play a game of tic-tac-toe. I've made a Python 3 program to learn more about how neural networks work.
When I enter a row with less than 9 values the program works fine. But when I enter a row with more than 9 values it fails. I believe it has to do with the neural network fitting to the initial weights. Any suggestions as to how I can work around this problem?
# Learn from game.
class NeuralNetwork():
    def __init__(self, rules):
        self.rules = rules
    # Calculate Q values for all possible states.
    def calculate_Q(self):
        for i in range(1,11):
            for j in range(1,11):
                for k in range(1,11):
                    # Pick up player and their state.
                    # State is the string of letters.
                    player = "X" if i>j else "O"
                    playerstate = str(i+1) + str(j+1)
                    # Pick up board position and player.
                    boardpos = i + "," + j
                    playerpos = (i + 1) + (j + 1)
                    # Calculate Q values. 3125841983
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