Cool, thanks. On Mon, Jul 24, 2017 at 10:30 AM, Gian-Carlo Pascutto <g...@sjeng.org> wrote:
> On 24-07-17 16:07, David Wu wrote: > > Hmm. Why would discounting make things worse? Do you mean that you > > want the top move to drop off slower (i.e. for the bot to take longer > > to achieve the correct valuation of the top move) to give it "time" > > to search the other moves enough to find that they're also bad? > > I don't want the top move to drop off slower, I just don't want to play > other moves until they've been searched to comparable "depth". > > If there's a disaster lurking behind the main-variation that we only > just started to understand, the odds are, the same disaster also lurks > in a few of the alternative moves. > > > I would have thought that with typical exploration policies, whether > > the top move drops off a little faster or a little slower, once its > > winrate drops down close to the other moves, the other moves should > > get a lot of simulations as well. > > Yes. But the goal of the discounting is, that a new move can make it > above the old one, despite having had less total search effort. > > My point is that it is not always clear this is a positive effect. > > > I know that there are ways to handle this at the root, via time > > control or otherwise. > > The situation isn't necessarily different here, if you consider that at > the root the best published technique is still "think longer so the new > move can overtake the old one", not "play the new move". > > Anyway, not saying this can't work. Just pointing out the problem areas. > > I would be a bit surprised if discounting worked for Go because it's > been published for other areas (e.g. Amazons) but I don't remember any > reports of success in Go. But the devil can be in the details (i.e. the > discounting formula) for tricks like this. > > -- > GCP > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go >
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