Hi all,
Our paper "Sample-Based Learning and Search with Permanent and
Transient Memories" has been accepted for publication at ICML 2008.
Once again, I would really appreciate any feedback or comments on the
paper.
http://www.cs.ualberta.ca/~silver/research/publications/files/dyna2.pdf
ICML is a technical conference on machine learning, and the paper
assumes some familiarity with machine learning terminology. Having
said that, I hope that the main ideas are understandable by everyone
on this list! In particular I've heard several people ask questions
like:
-How can we generalise between different positions during search?
-How can we learn "on the fly" about the value of different patterns?
-How can we combine learned knowledge with search?
-Are there more efficient algorithms than Monte-Carlo for updating the
value of states or patterns?
-What's so special about UCT? Can we get similar or better performance
by using other sample-based search algorithms?
The Dyna-2 architecture described in this paper provides an approach
for answering these questions.
Hopefully this will pique your interest enough to read the paper :-)
Thanks!
-Dave
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