[Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search
Lucas, Simon M
sml at essex.ac.uk
Thu Jan 28 07:41:01 PST 2016
Indeed – Congratulations to Google DeepMind!
It’s truly an immense achievement. I’m struggling
to think of other examples of reasonably mature
and strongly contested AI challenges where a new
system has made such a huge improvement over
existing systems – and I’m still struggling …
From: Computer-go [mailto:computer-go-bounces at computer-go.org] On Behalf Of Olivier Teytaud
Sent: 27 January 2016 20:27
To: computer-go <computer-go at computer-go.org>
Subject: Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search
Congratulations people at DeepMind :-)
I like the fact that alphaGo uses many forms of learning (as humans do!):
- imitation learning (on expert games, learning an actor policy);
- learning by playing (self play, policy gradient), incidentally generating games;
- use of those games for teaching a second deep network (supervised learning);
- real time learning with Monte Carlo simulations (including Rave ?).
==> just beautiful :-)
2016-01-27 21:18 GMT+01:00 Yamato <yamato_cg at yahoo.co.jp<mailto:yamato_cg at yahoo.co.jp>>:
Do you have a plan to run AlphaGo on KGS?
It must be a 9d!
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Olivier Teytaud, olivier.teytaud at inria.fr<mailto:olivier.teytaud at inria.fr>, TAO, LRI, UMR 8623(CNRS - Univ. Paris-Sud),
bat 490 Univ. Paris-Sud F-91405 Orsay Cedex France http://www.slideshare.net/teytaud
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