Move Evaluation in Go Using Deep Convolutional Neural Networks
Abstract
Abstract: 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. When the trained convolutional network was used directly to play games of Go, without any search, it beat the traditional search program GnuGo in 97% of games, and matched the performance of a state-of-the-art Monte-Carlo tree search that simulates a million positions per move.
Cite
Text
Maddison et al. "Move Evaluation in Go Using Deep Convolutional Neural Networks." International Conference on Learning Representations, 2015.Markdown
[Maddison et al. "Move Evaluation in Go Using Deep Convolutional Neural Networks." International Conference on Learning Representations, 2015.](https://mlanthology.org/iclr/2015/maddison2015iclr-move/)BibTeX
@inproceedings{maddison2015iclr-move,
title = {{Move Evaluation in Go Using Deep Convolutional Neural Networks}},
author = {Maddison, Chris J. and Huang, Aja and Sutskever, Ilya and Silver, David},
booktitle = {International Conference on Learning Representations},
year = {2015},
url = {https://mlanthology.org/iclr/2015/maddison2015iclr-move/}
}