From my own experience, an important testing case whenever trying
to get AMAF to work, is the scaling study.
My prototype was quite strong considering that it used only 1000 light playout
(and score 25-30 % win against gnugo lvl 0), but it seemed to not get much
over that as the number of playout grew ... (also there had a serious
exponential complexity problem, which i never get into the trouble of
investigating :) )
I know that Zoe was about 2000 elo with i think 50k simulations, and ... never
got any real better as the number of simulations increased.
Both prototype were toying with AMAF, so i really think you need a bit of
scalability
study whenever trying to asses an engine employing it. (although it could very
well be
that the scalability trouble came out of some nasty bugs. Both aforementioned
prototype
where quite messy ...)
> From: [EMAIL PROTECTED]
> Subject: Re: [computer-go] RAVE formula of David Silver (reposted)
> Date: Sat, 29 Nov 2008 03:39:58 -0200
> To: computer-go@computer-go.org
> CC:
>
>
> On 28-nov-08, at 17:28, [EMAIL PROTECTED] wrote:
>
> > I would be very interested to see the RAVE code from Valkyria. I'm
> > sure others would be too.
> >
>
> I'm much more interested in a general concise description. If such a
> description cannot be given easily, then I think there's little point
> including it in the definition of a MCTS reference engine.
>
> I found a serious flaw in my code collecting the AMAF scores, which
> explains why I wasn't seeing any gains so far with AMAF turned on.
> Now over the first 100+ games UCT+RAVE scores 90% over plain UCT. I'm
> going to run a test overnight, but so far it looks good. It should
> have collected a few thousand samples by tomorrow.
>
> Hopefully next week I can go back to testing my list of playout
> improvements, which is why I started making the MCTS reference
> implementation in the first place. This RAVE stuff caused a bit of a
> distraction, but it's nice to have if it works.
>
> Mark
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> computer-go mailing list
> computer-go@computer-go.org
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