Thumbnail

Residual Networks for Computer Go

Cazenave, Tristan (2018), Residual Networks for Computer Go, IEEE Transactions on Games, 10, 1, p. 107-110. 10.1109/TCIAIG.2017.2681042

View/Open
Type
Article accepté pour publication ou publié
Date
2018
Journal name
IEEE Transactions on Games
Volume
10
Number
1
Publisher
IEEE - Institute of Electrical and Electronics Engineers
Pages
107-110
Publication identifier
Metadata
Show full item record
Author(s)
Cazenave, Tristan
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
Deep learning for the game of Go recently had a tremendous success with the victory of AlphaGo against Lee Sedol in March 2016. We propose to use residual networks so as to improve the training of a policy network for computer Go. Training is faster than with usual convolutional networks and residual networks achieve high accuracy on our test set and a four dan level.
Subjects / Keywords
Computer Go; deep learning; residual networks