Move Evaluation in Go Using Deep Convolutional Neural Networks

@article{Maddison2015MoveEI,
  title={Move Evaluation in Go Using Deep Convolutional Neural Networks},
  author={Chris J. Maddison and Aja Huang and Ilya Sutskever and David Silver},
  journal={CoRR},
  year={2015},
  volume={abs/1412.6564}
}
The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function. In this paper we investigate whether deep convolutional networks can be used to directly represent and learn this knowledge. We train a large 12-layer convolutional neural network by supervised learning from a database of human professional games. The network correctly predicts the expert move in 55% of positions, equalling the accuracy of a 6 dan human player… CONTINUE READING
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