Machines that learn to play games
Application of reinforcement learning to the game of Othello
Computers and Operations Research
Spatial processing layer effects on the evolution of neural networks to play the game of Othello
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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Conventional Othello programs are based on a thorough analysis of the game, and typically employ sophisticated evaluation functions and supervised learning techniques that use large expert-labeled game databases. This paper presents an alternative method that trains a neural network to evaluate Othello positions via temporal difference (TD) learning. The approach is based on a network architecture that reflects the spatial and temporal organization of the problem domain. The network begins with random weights, and through self-play achieves an intermediate level of play. We also present a simple and effective method for analyzing what the network learned.