Jacques Basaldúa 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?
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.
Rémi
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