It's a good start. I haven't tested against gnugo on 9x9 for a long time so I tried a short test his afternoon.
Many Faces, 1000 playouts per move, vs Gnugo 3.7.10, level 10. Many Faces won 75.9% of 3669 games (+-1.4% confidence level). It took me about 300 versions tested to get from 10% wins against gnugo in 3 minute games to 90% wins against gnugo with 5K playouts, before I switched to 19x19. Many things you try won't make your program better, but if you keep trying it will get much better. David > -----Original Message----- > From: [email protected] [mailto:computer-go- > [email protected]] On Behalf Of Jacques Basaldúa > Sent: Thursday, December 30, 2010 1:57 PM > To: [email protected] > Subject: [Computer-go] Oakfoam and ELO Features > > Hi Francois, Welcome > > > > For reference I need about 100k playouts with > > RAVE to get 50% winrate against GnuGo 3.8 L10. > > Yes that's more or less expected. At least before the > "big" improvements (yet to come ;-) > > In my case I do a lot of testing at 4x10000 because > the games are around 15 seconds long and I get fast > Elo confidence intervals. At that rate 40 K plyo/move > I get about 40-42% of wins against gnugo. This is more > or less consistent with a debugged barebones without > particular smarts (but with RAVE, without progressive > widening). I guess 40% at 40000 scales to 50% near > 100K but the exact point where I reach 50% has not > been studied as I expect it to be much lower in the > near future. (Optimistic) > > > The next step is obviously to apply these to the > > playouts. I am currently testing my program with > > the ELO features in the playouts, but unfortunately > > the preliminary results don't look good. > > That's exactly my experience! Although you do get > improvement with extend from atari/capture/distance to > prev heuristic. > > The Mogo and CrazyStone papers report improvement > "all features included" which is true because the > other ideas produce improvement, but they don't > give results for the patterns in isolation. > > I got a lot of improvement from Rémi's Bradley-Terry > ideas in move prediction (although with some > overlearning which I didn't care much about as > predicting moves is not my interest.) But neither > the naif values (times played/times seen) nor the > improved Bradley-Terry values are better in playouts > than uniform random. They are 158 CI(114..202) Elo > points worse! > > That is good and bad news. Why should uniform random > be the best?. Obviously it is not. But what humans > play lacks all the information about what they don't > play because it is obvious to them, but it is not > obvious to a "silly" playout policy. > > How to find good values for the patterns? (What I have > tried.) > > a. Use small patterns (3x3) with all non-ill-formed > patterns in the database. (Other databases have a value > for "unknown" this one shouldn't.) > > b. Classify patterns. I have done that in 40 classes. > This way you reduce the amount of degrees of liberty. > So your vector of gamma values is in R^40 > > c. Then what? I really don't know. I have a "sort of > genetic algorithm". I like the idea that anything > changes at random, because the gammas are not > independent and this way the expected value of the > correlation is zero even under stochastic dependence. > Then I select the "best winners" and move my center of > gravity one little step in one or two classes of patterns > repeat the entire process. Then test to see if there > was improvement. A long process. I only won a little > in the first iterations. After tat fake improvement > that wasn't verified against uniform random. > > In all about 100 Elo points, less improvement than > the "humans patterns" do wrong. I guess best playouts > are a research area where there is room for improvement. > > > Jacques. > > > > _______________________________________________ > Computer-go mailing list > [email protected] > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go _______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
