Hi, Remi. I have a question about the "burn-in" process for CLOP.
Normally you need a lot of data to make a decent regression function. For example, if you have N arguments in your function, then CLOP (Correlated-All) needs 1 + N * (N+3) / 2 parameters. So if you want 10 observations per parameter, then you need 10 + 5N(N+3) samples. But even getting *one* sample can be tricky, because the 'logit' for a sample is +INF if the sample wins all of its games, and -INF if the sample loses all of its games. So you need a sample that has some wins and some losses. If the true value of the function is near 0.5, then the average number of trials required to obtain a sample is around 3, which is fine. But some of the test functions in your paper are very different. For example, the Correlated2 function is nearly 0 for most of the domain [-1,1]^4. When I sample randomly, it takes ~5K samples (that is, ~20K trials) to turn up enough samples to fit a regression line. I tried initializing my win/loss counters to epsilon instead of zero. But that technique was not robust, because any reasonable epsilon is actually larger than Correlated2 for most of the domain. Consequently, the "reduce the weights" step does not reduce enough weights, and the logistic regression ends up fitting epsilon, rather than Correlated2. So I cannot get a valid measurement with less than 20K trials before the first regression step. But your paper shows regret curves that start out at 10 trials. What am I missing? Thanks, Brian _______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
