Move evaluation in go using deep convolutional neural networks
Venue
ICLR, ICLR (2015)
Publication Year
2015
Authors
Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver
BibTeX
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.
