Learning to play Tic-Tac-Toe

To show how adaptable neural networks can be, we will now attempt to use a neural network in order to learn the optimal moves for Tic-Tac-Toe. We will approach this knowing that Tic-Tac-Toe is a deterministic game and that the optimal moves are already known.

Getting ready

To train our model, we will use a list of board positions followed by the optimal response for a number of different boards. We can reduce the amount of boards to train on by considering only board positions that are different with regard to symmetry. The non-identity transformations of a Tic-Tac-Toe board are a rotation (in either direction) of 90 degrees, 180 degrees, and 270 degrees, a horizontal reflection, and a vertical reflection. Given this idea, we will use a shortlist of boards with the optimal move, apply two random transformations, and then feed that into our neural network for learning.

Since Tic-Tac-Toe is a deterministic game, it is worth noting that...