[Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search (value network)
ds2 at physik.de
Thu Feb 4 09:21:33 PST 2016
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Thanks for the response, I do not refer to the finaly used data set:
in the referred chapter they state, they have used their kgs dataset
in a first try (which is in another part of the paper referred to
being a 6d+ data set).
Am 04.02.2016 um 18:11 schrieb Álvaro Begué:
> The positions they used are not from high-quality games. They
> actually include one last move that is completely random.
> On Thursday, February 4, 2016, Detlef Schmicker <ds2 at physik.de>
> I try to reproduce numbers from section 3: training the value
> 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,
> 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:
>>>> 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
>> _______________________________________________ Computer-go
> _______________________________________________ Computer-go mailing
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