A neural network that learns to play the board game of five-in-a-row is presented. The basic idea of the approach is to let an appropriately designed network play a series of games against an opponent and use a reinforcement learning algorithm to train the network to evaluate the non-occupied board positions by rewarding good moves and penalizing bad moves. The performance of the proposed network is demonstrated by presenting experimental results.
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