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

Detlef Schmicker ds2 at physik.de
Thu Feb 4 11:24:54 PST 2016


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>> Since all positions of all games in the dataset are used, winrate
>> should distributes from 0% to 100%, or -1 to 1, not 1. Then, the
>> number 70% could be wrong.  MSE is 0.37 just means the average
>> error is about 0.6, I think.

0.6 in the range of -1 to 1,

which means -1 (eg lost by b) games -> typical value -0.4
and +1 games -> typical value +0.4 of the value network

if I rescale -1 to +1 to  0 - 100% (eg winrate for b) than I get about
30% for games lost by b and 70% for games won by B?

Detlef


Am 04.02.2016 um 20:10 schrieb Hideki Kato:
> Detlef Schmicker: <56B385CE.4080804 at physik.de>: 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?
> 
>> Looks correct.
> 
> 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%
> ?!
> 
>> Since all positions of all games in the dataset are used, winrate
>> should distributes from 0% to 100%, or -1 to 1, not 1. Then, the
>> number 70% could be wrong.  MSE is 0.37 just means the average
>> error is about 0.6, I think.
> 
>> Hideki
> 
> 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 
>>>> http://computer-go.org/mailman/listinfo/computer-go
>>>> 
>> _______________________________________________ Computer-go
>> mailing list Computer-go at computer-go.org 
>> http://computer-go.org/mailman/listinfo/computer-go
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