On Thu, 2007-05-17 at 12:17 -0400, George Dahl wrote: > Imagine if you had a monte carlo program that took almost no time to > run. You would use it to do "heavy" playouts for another monte carlo > program to make it even stronger.
I tried something like this as a test with simple monte carlo. I called it recursive monte carlo. This was not UCT, it was just the simple statistic monte/carlo where you choose the single move that had the most success in totally random play-outs. This started when I noticed that even running 1 or 2 simulations gave far better play than random. So my idea was that instead of choosing moves randomly in the play-outs, I would choose each move in the playout by doing another set of play-outs - applying the same simulation idea recursively. I didn't expect it to be fast enough to be practical, but I thought that at least I could see if the idea was feasible. If I'm doing 1000 play-outs, the version that does the sub-play-outs should be far stronger than the one that didn't even if it's a lot slower. Instead, it was much slower and played worse. I didn't pursue this any farther, but I suspect that it should have worked. I think the problem is that I wasn't getting fair representation of each move. The sub-playouts were causing some moves not to get sampled much for instance. I am guessing that if I used such a technique for the play-out portion of the monte-carlo search, it would be incredibly strong for a given level if I only considered the number of play-outs of the outer-level. - Don _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
