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Abstract: Standard value function approaches to finding policies for Partially Observable Markov Decision
Processes (POMDPs) are generally considered to be intractable for large models. The intractability
of these algorithms is to a large extent a consequence of computing an exact, optimal
policy over the entire belief space. However, in real-world POMDP problems, computing the optimal
policy for the full belief space is often unnecessary for good control even for problems with
complicated policy... (Update)
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BibTeX entry: (Update)
Nicholas Roy. Finding approximate POMDP solutions through belief compression. PhD Thesis Proposal, Carnegie Mellon University, 2000. http://citeseer.ist.psu.edu/roy00finding.html More
@misc{ roy00finding,
author = "N. Roy",
title = "Finding approximate POMDP solutions through belief compression",
text = "Nicholas Roy. Finding approximate POMDP solutions through belief compression.
PhD Thesis Proposal, Carnegie Mellon University, 2000.",
year = "2000",
url = "citeseer.ist.psu.edu/roy00finding.html" }
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