On 23-07-17 18:24, David Wu wrote: > Has anyone tried this sort of idea before?
I haven't tried it, but (with the computer chess hat on) these kind of proposals behave pretty badly when you get into situations where your evaluation is off and there are horizon effects. The top move drops off and now every alternative that has had less search looks better (because it hasn't seen the disaster yet). You do not want discounting in this situation. It's true that a move with a superior winrate than the move with the maximum amount of simulations is a good candidate to be better. Some engines will extend the time when this happens. Leela will play it, in certain conditions. > I recall a paper published on this basis. A paper presumably about > CrazyStone: Efficient Selectivity and Backup Operators in > Monte-Carlo Tree Search. I'm reasonably sure this did not include forgetting/discounting, only shifting between average and maximum by focusing simulations near the maximum. It's the predecessor of UCT. -- GCP _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
