Indeed. My plan is to generalize this scheme with more strict
conditions. Maybe we even can combine LGRF with the information of
RAVE (inspired from Arpad Rimmel's works). If the learning works well,
it should fix a lot of errors in my rules of the playout features.
This might be a way to make the playouts to learn how to play correct
semeai moves.
If you know in a playout that the best reply has to be one of only a
handful of options (e.g. in a semeai), non-zero probabilities for all of
those moves plus LGRF should be a nice way of making the search adapt.
But whenever you make the conditions more specific, be aware that you
will get fewer samples per condition, and you need lots of samples to be
able to count on good replies staying in the table longer than bad replies.
If you could find an efficient way of combining LGRF with local
patterns, I would see some potential. I couldn't get it to run fast enough.
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