I imagine that at low visits at least, "ACT" behaves similarly to Leela
Zero's "LCB" move selection, which also has the effect of sometimes
selecting a move that is not the max-visits move, if its value estimate has
recently been found to be sufficiently larger to balance the fact that it
is lower prior and lower visits (at least, typically, this is why the move
wouldn't have been the max visits move in the first place). It also scales
in an interesting way with empirical observed playout-by-playout variance
of moves, but I think by far the important part is that it can use
sufficiently confident high value to override max-visits.

The gain from "LCB" in match play I recall is on the very very rough order
of 100 Elo, although it could be less or more depending on match conditions
and what neural net is used and other things. So for LZ at least,
"ACT"-like behavior at low visits is not new.


On Sun, Jul 19, 2020 at 5:39 AM Kensuke Matsuzaki <knsk.m...@gmail.com>
wrote:

> Hi,
>
> I couldn't improve leela zero's strength by implementing SEARCH and ACT.
> https://github.com/zakki/leela-zero/commits/regularized_policy
>
> 2020年7月17日(金) 2:47 Rémi Coulom <remi.cou...@gmail.com>:
> >
> > This looks very interesting.
> >
> > From a quick glance, it seems the improvement is mainly when the number
> of playouts is small. Also they don't test on the game of Go. Has anybody
> tried it?
> >
> > I will take a deeper look later.
> >
> > On Thu, Jul 16, 2020 at 9:49 AM Ray Tayek <rta...@ca.rr.com> wrote:
> >>
> >>
> https://old.reddit.com/r/MachineLearning/comments/hrzooh/r_montecarlo_tree_search_as_regularized_policy/
> >>
> >>
> >> --
> >> Honesty is a very expensive gift. So, don't expect it from cheap people
> - Warren Buffett
> >> http://tayek.com/
> >>
> >> _______________________________________________
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>
>
> --
> Kensuke Matsuzaki
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