Maybe I did no explain my point well enough.
The problem with infinite is that we get a better approximation to a
wrong value.
With few simulations we get that value with, say 1/10 error. With an
astronomical amount
of simulations we get the same value with an error of 1e-200, but it's
still wrong!. It is
proved that simulating a go position converges, but it does not converge
to the same
value as if the position was played by perfect players, it only
converges to the asymptotic
limit of random play.
I am not an MC developer, but as far as I know, UCT keeps a limited
(i.e. n-ply) tree
in memory and intentionally unbiasses the nodes to make the convergence
faster, that
does not change anything, assuming constant tree size.
A simple test :
1: after 100 simulations, choose the highest number in (0.96, 2.1, 3.18)
2: after 1e9 simulations, choose it in (0.9999999, 2.0000001, 3.000001)
You chose the same value (= same move).
That's why, I insist, if you don't increase the size of the tree and
only get a better
approximation to a wishful but frequently misconceived value (the limit
of random
play) witch is *not* a good evaluation of the game, you don't
significantly improve
your play. Of course, if you increase the tree, you reach perfect play,
that's not
the point.
Jacques.
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