[Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

Erik van der Werf erikvanderwerf at gmail.com
Mon Dec 15 06:59:08 PST 2014

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>
> Hi,
> This paper looks very cool.
> Teaching Deep Convolutional Neural Networks to Play Go
> http://arxiv.org/pdf/1412.3409v1.pdf
> Thier move prediction got 91% winrate against GNU Go and 14%
> against Fuego in 19x19.
> Regards,
> Hiroshi Yamashita
> _______________________________________________
> Computer-go mailing list
> Computer-go at computer-go.org
> http://computer-go.org/mailman/listinfo/computer-go
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