On Wed, Dec 16, 2009 at 08:57:24PM +0100, Rémi Coulom wrote:
> The problem is that I am not using the same set of features for
> biasing the tree, and for playouts. Playouts only use fast light
> features. The tree part uses slow complex features. In particular, I
> use patterns of radius 3 and 4 in the tree, and only radius 3 in the
> playouts. When 3x3 patterns are learnt together with r=4 patterns,
> they get different gammas.
Interesting, your paper said that you are using patterns up to r=10,
did you find out that anything larger than r=4 is irrelevant in
practice?
I have trouble even *nearing* the performance you reported; you say
you can play 13500 games per second, do you have any data on how fast
your engine runs with uniformly random playouts to put this in scale?
My engine does 20k games/s with random playouts, 10k games/s with
random playouts and board implementation incrementally maintaining 3x3
patterns, and 1600 games/s when using probability distribution. There
is some room for optimization, but not to reach 13k games/s...
So I wonder if you have any tips for fast implementation of the
probability distribution based simulations? Do you maintain the
probability distribution itself incrementally over moves, or only
the shape features?
Thanks,
--
Petr "Pasky" Baudis
A lot of people have my books on their bookshelves.
That's the problem, they need to read them. -- Don Knuth
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