I want to be sure that I understand how distance affects Erica's RAVE
heuristic.
The generic code for updating a weighted average would look something like
this:
RAVEDenominator[move] += weight;
RAVENumerator[move] += credit * weight;
In standard RAVE, credit is a Win or Loss (1 or 0) and weight = 1.
In your thesis, the description says "If the simulation outcome is 1, then
the updated outcome is 1-d*w; if the simulation outcome is 0 then the
updated outcome is 0+d*w"
How would I understand this in terms of credit and weight in the weighted
average code above?
Thanks,
Brian
_____
From: [email protected]
[mailto:[email protected]] On Behalf Of Aja
Sent: Wednesday, July 27, 2011 8:27 AM
To: [email protected]
Subject: [Computer-go] Aja's PhD thesis
Dear all,
If you are interested, my PhD thesis, entitled "New Heuristics for Monte
Carlo Tree Search Applied to the Game of Go", can be found in the following
link.
<http://www.grappa.univ-lille3.fr/~coulom/Aja_PhD_Thesis.pdf>
http://www.grappa.univ-lille3.fr/~coulom/Aja_PhD_Thesis.pdf
Due to some personal reasons, I am sorry to announce that the sharing of
Erica's binary is indefinitely postponed.
Best regards,
Aja
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