On Sun, Jun 13, 2010 at 08:55:16PM -0500, Daniel Liu wrote:
> This post is to propose a metric that measures the effectiveness of a
> playout policy
> in a MC tree search. It could give some idea as how the playing strength
> varies with
> the total playout number.
>
> Let N be the total playout number. The effectve search depth is defined as
>
> Depth = (log with base f) (N/m),
> where m is related to factors such as the threshold value used, etc. f is
> the more
> interestng number characterizing a playout policy. A playout that selects
> moves
> randomly gives the largest value of f. I thnk it could be 2 or bigger. For
> the most
> effective search policy available today, such as those used by the most
> strong Go programs
> at present is about 1.5.
>
> So what can above calculation tell us? According to above calculation it
> could estimate
> that the effective search depth of the today's strong Go programs are about
> 11, if the playout
> number is one million and assume m=600, f=1.5. If an effective search depth
> of 50
> is requied to reach high dan level. Then the playout number needs to
> increase by a
> factor of 1.5^39, about 7.4 milliom times. That is 7.4 trillion playouts s
> neeeded.
Hi!
Frankly, I'm a bit puzzled here. You present a completely
arbitrary-looking formula and completely arbitrary-looking values of
some mysterious constants and then try to conjecture something from
that.
What does the logarithm express? Exactly how is m constructed and what
is its meaning? How to determine f, why would it be the values you say
it is? Does node selection policy (e.g. RAVE) matter to your formula?
--
Petr "Pasky" Baudis
The true meaning of life is to plant a tree under whose shade
you will never sit.
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