Learning position evaluation for Go with Internal Symmetry Networks

We develop a cellular neural network architecture consisting of a large number of identical neural networks organised in a cellular array, and introduce a novel weight sharing scheme based on the principle of internal symmetry from particle physics. This internal symmetry network is then trained by self-play and temporal difference learning to perform position evaluation for the game of Go.