RE: MC + NN feedback: One area I'm particularly interested in is using NN to apply knowledge from the tree during the playout. I expect that NNs will have difficulty learning strong tactical play, but a combination of a pre-trained network with re-training based on the MCTS results might be able to apply the knowledge gained in MCTS during the playout to correctly resolve L&D situations, semeai, and maybe ko fights. Does anyone else have interest in this?
-Mark On Mon, Dec 15, 2014 at 3:58 PM, Aja Huang <ajahu...@gmail.com> wrote: > 2014-12-15 23:29 GMT+00:00 Petr Baudis <pa...@ucw.cz>: >> >> Huh, aren't you? >> >> I just played quick two games GnuGoBot39 where I tried very hard not >> to read anything at all, and had no trouble winning. (Well, one of my >> groups had some trouble but mindless clicking saved it anyway.) > > > That well explains your level is far beyond GnuGo, probably at least 3k on > KGS. > > That being said, Hiroshi, are you sure there was no problem in your > experiment? 6% winning rate against GnuGo on 19x19 seems too low for a > predictor of 38.8% accuracy. And yes, in the paper we will show a game that > the neural network beat Fuego (or pachi) at 100k playouts / move. > > Aja > > _______________________________________________ > 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
