Since patterns are correlated with each other, the gamma sets are
specific to the pattern set used. Since more patterns are used in the
tree, itrequires a separate set of gammas than the in-tree search.
Sent from my iPhone
On Dec 16, 2009, at 2:50 PM, Jacques Basaldúa <jacq...@dybot.com>
wrote:
> Petr Baudis wrote:
> > I wonder now, do you use separate set of gammas for simulations
and node
> > biasing? Since I've found that the performance seems very bad if
I don't
> > include some time-expensive features, since the gammas are then
very
> > off; I will probably simply generate two gamma sets, but perhaps
it's
> > enough to do some trick like merging features by computing
weighted
> > (geometric?) averages?
> Rémi Coulom answered:
> I learn two sets of gammas separately for the two sets of features.
I don't get it. Why do you need two sets one for the tree and one
for the playouts? To learn gammas, I use a database of games.
The patterns compete, some of them win. This is computed using a
Bradley-Terry model. At that time moves are just moves, not tree moves
or simulation moves. When that offline learned model 'best' fits the
moves played (55000 games x 100 moves each in my case) I am done, I
have a set of gamma values.
I use these for playouts and biasing the tree. What else are you
doing?
How do you compute a set for the playouts and a set for the tree?
Do you adjust gamma values one by one playing games?
Of course, I guess this is not very useful for 9x9 that's why I took
the (probably wrong) decision to work in 19x19 only.
Jacques.
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