[Computer-go] Teaching Deep Convolutional Neural Networks to Play Go
ajahuang at google.com
Mon Dec 15 07:16:03 PST 2014
Chris Maddison also produced very good (in fact much better) results using
a deep convolutional network during his internship at Google. Currently
waiting for publication approval, I will post the paper once it is passed.
On Mon, Dec 15, 2014 at 2:59 PM, Erik van der Werf <erikvanderwerf at gmail.com
> Thanks for posting this Hiroshi!
> Nice to see this neural network revival. It is mostly old ideas, and it is
> not really surprising to me, but with modern compute power everyone can now
> see that it works really well. BTW for some related work (not cited),
> people might be interested to read up on the 90s work of Stoutamire,
> Enderton, Schraudolph and Enzenberger.
> Comparing results to old publications is a bit tricky. For example, the
> things I did in 2001/2002 are reported to achieve around 25% prediction
> accuracy, which at the time seemed good but is now considered unimpressive.
> However, in hindsight, an important reason for that number was time
> pressure and lack of compute power, which is not really related to anything
> fundamental. Nowadays using nearly the same training mechanism, but with
> more data and more capacity to learn (i.e., a bigger network), I also get
> pro-results around 40%. In case you're interested, this paper
> http://arxiv.org/pdf/1108.4220.pdf by Thomas Wolf has a figure with more
> recent results (the latest version of Steenvreter is still a little bit
> better though).
> Another problem with comparing results is the difficulty to obtain
> independent test data. I don't think that was done optimally in this case.
> The problem is that, especially for amateur games, there are a lot of
> people memorizing and repeating the popular sequences. Also, if you're not
> careful, it is quite easy to get duplicate games in you dataset (I've had
> cases where one game was annotated in chinese, and the other (duplicate) in
> English, or where the board was simply rotated). My solution around this
> was to always test on games from the most recent pro-tournaments, for which
> I was certain they could not yet be in the training database. However, even
> that may not be perfect, because also pro's play popular joseki, which
> means there will at least be lots of duplicate opening positions.
> I'm not surprised these systems now work very well as stand alone players
> against weak opponents. Some years ago David and Thore's move predictors
> managed to beat me once in a 9-stones handicap game, which indicates that
> also their system was already stronger than GNU Go. Further, the version of
> Steenvreter in my Android app at its lowest level is mostly just a move
> predictor, yet it still wins well over 80% of its games.
> In my experience, when the strength difference is big, and the game is
> even, it is usually enough for the strong player to only play good shape
> moves. The move predictors only break down in complex tactical situations
> where some form of look-ahead is critical, and the typical shape-related
> proverbs provide wrong answers.
> On Mon, Dec 15, 2014 at 12:53 AM, Hiroshi Yamashita <yss at bd.mbn.or.jp>
>> This paper looks very cool.
>> Teaching Deep Convolutional Neural Networks to Play Go
>> Thier move prediction got 91% winrate against GNU Go and 14%
>> against Fuego in 19x19.
>> Hiroshi Yamashita
>> Computer-go mailing list
>> Computer-go at computer-go.org
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> Computer-go at computer-go.org
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