Don Dailey wrote: > Matthew Woodcraft wrote: >> That doesn't seem to directly support deriving information from >> random trials. For computer go tuning, would you play multiple games >> with each parameter set in order to get a meaningful figure? That >> seems likely to be less efficient than treating it as a bandit >> problem.
> This does not replace bandit, it's a way to tune parameters. Err, yes. I know that. > You might have 50 parameters and so you play a few thousand games > using random combinations of these parameters for instance. And then > you have data based on the win/loss records of the different > parameters and use this to settle on a "good" set of parameters to be > used. Right so far. Further, it's useful to concentrate your efforts on the combinations of parameters which are looking most promising. So it's related to bandit problems (you can view it as a bandit with a rather large number of arms). -M- _______________________________________________ computer-go mailing list [email protected] http://www.computer-go.org/mailman/listinfo/computer-go/
