Hi!
On Thu, Sep 30, 2010 at 03:05:08PM +0200, Rémi Coulom wrote:
> This is the CG'2010 paper Aja wrote with me.
>
> Abstract: Simulation balancing is a new technique to tune parameters of a
> playout policy for a Monte-Carlo game-playing program. So far, this algorithm
> had only been tested in a very artificial setting: it was limited to 5x5 and
> 6x6 Go, and required a stronger external program that served as a supervisor.
> In this paper, the effectiveness of simulation balancing is demonstrated in a
> more realistic setting. A state-of-the-art program, Erica, learned an
> improved playout policy on the 9x9 board, without requiring any external
> expert to provide position evaluations. Evaluations were collected by letting
> the program analyze positions by itself. The previous version of Erica
> learned pattern weights with the minorization-maximization algorithm. Thanks
> to simulation balancing, its playing strength was improved from a winning
> rate of 69% to 78% against Fuego 0.4.
>
> You can download it from there:
> http://remi.coulom.free.fr/CG2010-Simulation-Balancing/
This is extremely interesting paper, thanks for making it available!
I was working towards something similar, but this is very valuable.
I'd like to ask about couple of unclear things:
* Did you train for all possible 3x3 pattern combinations? I didn't
have the time yet to check what 2100 base patterns sum up to. ;-)
* More importantly - how do you handle tenuki? Does the playout
*always* play a local move if any is available, or do you include tenuki
with some value within the probability distribution?
* Your ko feature seems a bit strange. Did you find that including ko
solution by connecting the ko is not beneficial and it is better to
restrict it only to captures? Overally, does the inclusion of the ko
feature make a large impact on playing strength?
Thanks,
--
Petr "Pasky" Baudis
The true meaning of life is to plant a tree under whose shade
you will never sit.
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