The Deepmind paper has a short section on the Rollout Policy they use, it looks like they made some improvements on for their rollouts, maybe they are better at handling semeai than previous methods. The response and non-response patterns sound similar, but they also include liberty counts. I don't remember that being included in classic Mogo 3x3 patterns.
They also talk about caching moves from the search tree. So if the tree has already found the correct answers to sente moves in a semeai, they could be applied in the rollout as well. Has anyone tried techniques similar to these? On Tue, Feb 2, 2016 at 2:02 PM, Hiroshi Yamashita <y...@bd.mbn.or.jp> wrote: > Hi Michael, > > It's an intersting idea. > Maybe I could collect many LD positions from pro games. > So LD and semeai solver will be needed. Or just use LD > problems with sequence. > Without difficult feature, DCNN may find answer. > > Regards, > Hiroshi Yamashita > > ----- Original Message ----- From: "Michael Sué" <michael....@gmx.de> > To: <computer-go@computer-go.org> > Sent: Wednesday, February 03, 2016 4:39 AM > Subject: Re: [Computer-go] DCNN can solve semeai? > > > Hi, >> >> I would expect this to happen if the system is trained by normal games, >> only. But I think the system should see actual live-and-death (LD) >> sequences (from some collection) to be able to learn about them and not >> soak this knowledge up from a whole game where most of the moves are >> "noise" compared to what you ask it to do later. >> So the training data could be half and half normal games and LD sequences. >> >> - Michael. >> >> _______________________________________________ >> Computer-go mailing list >> Computer-go@computer-go.org >> http://computer-go.org/mailman/listinfo/computer-go >> > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go >
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