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Residual Networks for Computer Go

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Date
2018
Dewey
Informatique générale
Sujet
Computer Go; deep learning; residual networks
Journal issue
IEEE Transactions on Games
Volume
10
Number
1
Publication date
03-2018
Article pages
107-110
Publisher
IEEE - Institute of Electrical and Electronics Engineers
DOI
http://dx.doi.org/10.1109/TCIAIG.2017.2681042
URI
https://basepub.dauphine.fr/handle/123456789/18637
Collections
  • LAMSADE : Publications
Metadata
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Author
Cazenave, Tristan
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Article accepté pour publication ou publié
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.

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