On 7/18/06, Jonathan Kinsey <[EMAIL PROTECTED]> wrote:
Ian Shaw wrote:
> Astonishingly, it's been about three years since version 0.14 of gnubg was
released. It has proved to be superior to JellyFish and at least the equal of
Snowie 4. Since then, BgBlitz has arrived as a serious opponent, and rumours of
Z-bot's approach persist. If it ever arrives, I'm sure it will be a strong player.
>
> I think we've rested on our laurels long enough, and it's about time we
started trying to improve the playing strength of our favourite bot.
>
> I can think of several ways where might seek to make improvements:
>
> A) Speed up the evaluation function so gnubg can search faster, and maybe
deeper.
> B) Improve the evaluation function by changing the neural net inputs or
hidden nodes.
> C) Retrain the existing net using a new set of training positions.
> D) Retrain the existing net using newer rollouts of the current set of
training positions.
Is having more neural nets a good idea? The race net does seem nearly
perfect,
the crashed net is quite specialised,
Yes but was developed by isolating a case where gnubg was provably not
playing well.
this seems to leave a lot
of positions for the contact net (the vast majority I guess). If we
split the contact positions up into several/lots of different
categorises (e.g. back games, holding games, prime positions) would this
produce a stronger bot?
I've deliberately side-stepped how you would exactly define these types
of positions
I am glad your explicitly mention this because it is the whole crux of
the matter. Past experience tell us that classifying positions in
groups whose graph contain cycles is gets very tricky when it comes to
getting the nets work well together. while it may work, previous
attempts to do so has failed.
and also the worked involved... Just wondered if it was a
direction worth considering?
Jon
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