I'm curious, does anybody have any interest in programs for 23x23 (or
larger) Go boards?
On Fri, Feb 23, 2018 at 8:58 AM, Erik van der Werf <erikvanderw...@gmail.com
> In the old days I trained separate move predictors on 9x9 games and on
> 19x19 games. In my case, the ones trained on 19x19 games beat the ones
> trained on 9x9 games also on the 9x9 board. Perhaps it was just because of
> was having better data from 19x19, but I thought it was interesting to see
> that the 19x19 predictor generalized well to smaller boards.
> I suppose the result you see can easily be explained; the big board policy
> learns about large scale and small scale fights, while the small board
> policy doesn't know anything about large scale fights.
> On Fri, Feb 23, 2018 at 5:11 PM, Hiroshi Yamashita <y...@bd.mbn.or.jp>
>> Using 19x19 policy on 9x9 and 13x13 is effective.
>> But opposite is?
>> I made 9x9 policy from Aya's 10k playout/move selfplay.
>> Using 9x9 policy on 13x13 and 19x19
>> 19x19 DCNNAyaF128from9x9 1799
>> 13x13 DCNNAyaF128from9x9 1900
>> 9x9 DCNN_AyaF128a558x1 2290
>> Using 19x19 policy on 9x9 and 13x13
>> 19x19 DCNN_AyaF128a523x1 2345
>> 13x13 DCNNAya795F128a523 2354
>> 9x9 DCNN_AyaF128a523x1 2179
>> 19x19 policy is similar strength on 13x13 and 166 Elo weaker on 9x9.
>> 9x9 policy is 390 Elo weaker on 13x13, and 491 Elo weaker on 19x19.
>> It seems smaller board is more useless than bigger board...
>> All programs select maximum policy without search.
>> All programs use opening book.
>> 19x19 policy is Filter128, Layer 12, without Batch Normalization.
>> 9x9 policy is Filter128, Layer 11, without Batch Normalization.
>> 19x19 policy is made from pro 78000 games, GoGoD.
>> 9x9 policy is made from 10k/move. It is CGOS 2892(Aya797c_p1v1_10k).
>> Ratings are BayesElo.
>> Hiroshi Yamashita
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