ivan dubois wrote:
I dont understand how you can reduce the variance of monte-carlo sampling,
given a simulation can return either 0(loss) or 1(win).
Maybe it means trying to have mean values that are closer to 0 or 1 ?
Well strictly speaking I agree the standard models don't fit that well
- the application of monte carlo to go is much different than
traditional applications. However, imagine the whole path of a
simulation to the leaf as a meaningful set of points. We are only
measuring the end, but the path is very important too.
Also, as you mentioned one could target the larger scoped variance of
the set of simulations mean value correctly classifying the point as a
win or loss.
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