> I tried this yesterday with K=10 and it seemed to make Many Faces weaker > (84.2% +- 2.3 vs 81.6% +-1.7), not 95% confidence, but likely weaker. This > is 19x19 vs gnugo with Many Faces using 8K playouts per move, 1000 games > without and 2000 games with the change. I have the UCT exploration term, so > perhaps with exploration this idea doesn't work. Or perhaps the K I tried > is too large.
If I understood correctly Olivier described it (*) as being the most important term when using a large number of simulations. How about trying 8 million playouts instead of 8 thousand... Which brings up the question of how to reliably evaluate changes when using a high number of playouts. Darren *: Olivier wrote: What we call "progressive unpruning" is termed "progressive bias" by Rémi Coulom. It is the use of a score which is a linear combination between 1) expert knowledge 2) patterns (in 19x19) 3) rave values 4) regularized success rate (nbWins +K ) /(nbSims + 2K) (the original "progressive bias" is simpler than that) for small numbers of simulations, 1) and 2) are the most important; 3) become important later; and 4) is, later, the most important term. -- Darren Cook, Software Researcher/Developer http://dcook.org/gobet/ (Shodan Go Bet - who will win?) http://dcook.org/mlsn/ (Multilingual open source semantic network) http://dcook.org/work/ (About me and my work) http://dcook.org/blogs.html (My blogs and articles) _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
