Deep Preference Neural Network for Move Prediction in Board Games

Abstract

The training of deep neural networks for move prediction in board games using comparison training is studied. Specifically, the aim is to predict moves for the game Othello from championship tournament game data. A general deep preference neural network will be presented based on a twenty year old model by Tesauro. The problem of over-fitting becomes an immediate concern when training the deep preference neural networks. It will be shown how dropout may combat this problem to a certain extent. How classification test accuracy does not necessarily correspond to move accuracy is illustrated and the key difference between preference training versus single-label classification is discussed. The careful use of dropout coupled with richer game data produces an evaluation function that is a better move predictor but will not necessarily produce a stronger game player.