[Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)

Álvaro Begué alvaro.begue at gmail.com
Thu Feb 4 09:11:53 PST 2016


The positions they used are not from high-quality games. They actually
include one last move that is completely random.

Álvaro.


On Thursday, February 4, 2016, Detlef Schmicker <ds2 at physik.de> wrote:

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> Hi,
>
> I try to reproduce numbers from section 3: training the value network
>
> On the test set of kgs games the MSE is 0.37. Is it correct, that the
> results are represented as +1 and -1?
>
> This means, that in a typical board position you get a value of
> 1-sqrt(0.37) = 0.4  --> this would correspond to a win rate of 70% ?!
>
> Is it really true, that a typical kgs 6d+ position is judeged with
> such a high win rate (even though it it is overfitted, so the test set
> number is to bad!), or do I misinterpret the MSE calculation?!
>
> Any help would be great,
>
> Detlef
>
> Am 27.01.2016 um 19:46 schrieb Aja Huang:
> > 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:
> >
> > http://www.deepmind.com/alpha-go.html
> >
> > 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!
> >
> > Aja
> >
> > 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 <javascript:;>
> > http://computer-go.org/mailman/listinfo/computer-go
> >
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