Hi greenpeep aka chris, My program GGMC Go ver. 2, rated around 2000 ELO now, runs abut 25k playouts/s on 4-core box and do 360k playouts/move at most on cgos (and last KGS tournament as well). It's based on MoGo's first report, though its framework is different. # I'll add some features but have no time to... :-(
-gg (Hideki) Christopher Rosin: <[EMAIL PROTECTED]>: >Hi - yes, that is me, and greenpeep is my program. About 10 years ago >I worked on coevolution applied to Go, but greenpeep is an >entirely new program based on UCT. I think the greenpeep is mostly >similar to what some other people are doing with UCT, and I'm using >it to test ideas. greenpeep uses the usual UCT, plus >all-moves-as-first based mostly on the MoGo paper from ICML 2007. > >greenpeep also uses patterns derived from 20000 UCT self-play games. >These are simple local patterns with scores that (roughly) indicate >the probability that the move at the center of the pattern was >selected by UCT during these games. These patterns are then used both >to bias moves at UCT nodes which have few visits, and also to bias the >playouts. What I've seen is: >- Biasing playouts by patterns is much better than unbiased playouts >- Playouts using self-play patterns together with MoGo-style move > preferences (favor defensive moves and captures, as well as local > moves biased by the self-play patterns, before resorting to a global > move biased by patterns) yield much better results than just using > the patterns by themselves globally. > >I tried to do some comparisons to MoGo's hand-coded local patterns as >described in the original MoGo report, and the self-play patterns >seemed to give overall results that are at least comparable. But I >think that there is a lot of room for improvement here. The fact that >it is possible to improve on the patterns by forcing additional simple >preferences like captures, means the patterns are certainly not as >good as they could be. Also, it was necessary to "flatten" the >pattern probabilities quite a bit; the quality of the patterns doesn't >seem to be good enough to bias moves too strongly. > >greenpeep uses some other tweaks to improve results, but nothing in the >current version that by itself had any large effect. > >greenpeep on CGOS and KGS has run on an 8-core machine, which >certainly helps a lot. I don't think the playouts are especially fast >though; the lookups into a large pattern table are one bottleneck. >The version on CGOS uses about 500k playouts/move in the opening, then >quickly goes down to about 250k playouts/move. > >I'm curious as to how many playouts other UCT/Monte Carlo programs >on CGOS are typically using. > >-Chris Rosin > >On 10/11/07, terry mcintyre <[EMAIL PROTECTED]> wrote: >> >> This may be the same Chris Rosin: >> >> http://www.cs.wisc.edu/areas/ai/aisem/abstracts/1995.2.summer/rosin.html >> http://www-cse.ucsd.edu/users/crosin/ >> >> Other than the senseis.xmp reference, I have been able to google nothing >> about greenpeep. >> >> Terry McIntyre <[EMAIL PROTECTED]> >> >> ----- Original Message ---- >> From: Olivier Teytaud <[EMAIL PROTECTED]> >> >> Following this idea of the "public" nature of experiments in cgos, >> I am very interested in greenpeep ("playouts guided by >> patterns extracted from offline self-play", according to >> http://senseis.xmp.net/?ComputerGoServer#toc33), I would be >> very >> grateful if someone could provide links/infos about it, it is seemingly >> quite innovative as it introduces an original way of learning across >> games (an efficient coevolution in Monte-Carlo planning would be >> great!). >> >> >> >> ________________________________ >> Yahoo! oneSearch: Finally, mobile search that gives answers, not web links. >> _______________________________________________ >> computer-go mailing list >> computer-go@computer-go.org >> http://www.computer-go.org/mailman/listinfo/computer-go/ >> >_______________________________________________ >computer-go mailing list >computer-go@computer-go.org >http://www.computer-go.org/mailman/listinfo/computer-go/ -- [EMAIL PROTECTED] (Kato) _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
