Hello list,
I am a Master's student currently working on his thesis about certain
aspects of Monte-Carlo Go. I would like to pose a question concerning
the literature - I hope some of you can help me out!
My problem is that I can't find many papers about learning of MC playout
policies, in particular patterns. A lot of programs seem to be using
Mogo's 3x3 patterns, which have been handcoded, or some variation
thereof. A lot of people have tried some form of pattern learning, but
mostly to directly predict expert moves it seems, not explicitly
optimizing the patterns for their function in an MC playout policy.
Actually, I am only aware of "Computing Elo Ratings of Move Patterns in
the Game of Go", where patterns have been learned from pro moves, but
then also successfully used in an MC playout policy; and "Monte Carlo
Simulation Balancing".
Considering the huge impact local patterns have had on the success of MC
programs, I would have expected more attention towards automatically
learning and weighting them specifically for MC playouts. There is no
reason why patterns which are good for predicting experts should also be
good for guaranteeing diverse, balanced playout distributions. Have I
missed something?
Or how did your program come to its patterns? I'd be interested. Did you
maybe even try learning something else than patterns for your playout
policy?
cheers,
Hendrik
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