[Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search
alvaro.begue at gmail.com
Mon Feb 1 13:45:14 PST 2016
I read the paper with great interest. [Insert appropriate praises here.]
I am trying to understand the part where you use reinforcement learning to
improve upon the CNN trained by imitating humans. One thing that is not
explained is how to determine that a game is over, particularly when a
player is simply a CNN that has a probability distribution as its output.
Do you play until every point is either a suicide or looks like an eye? Do
you do anything to make sure you don't play in a seki?
I am sure you are a busy man these days, so please answer only when you
On Wed, Jan 27, 2016 at 1:46 PM, Aja Huang <ajahuang at google.com> wrote:
> Hi all,
> We are very excited to announce that our Go program, AlphaGo, has beaten a
> professional player for the first time. AlphaGo beat the European champion
> Fan Hui by 5 games to 0. We hope you enjoy our paper, published in Nature
> today. The paper and all the games can be found here:
> AlphaGo will be competing in a match against Lee Sedol in Seoul, this
> March, to see whether we finally have a Go program that is stronger than
> any human!
> PS I am very busy preparing AlphaGo for the match, so apologies in advance
> if I cannot respond to all questions about AlphaGo.
> Computer-go mailing list
> Computer-go at computer-go.org
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