Article: Perseus: Randomized Point-based Value Iteration for POMDPs

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Aug 14, 2005, 10:59:48 AM8/14/05
JAIR is pleased to announce the publication of the following article:

Spaan, M.T.J. and Vlassis, N. (2005)
"Perseus: Randomized Point-based Value Iteration for POMDPs",
Volume 24, pages 195-220.

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Partially observable Markov decision processes (POMDPs) form an
attractive and principled framework for agent planning under
uncertainty. Point-based approximate techniques for POMDPs compute a
policy based on a finite set of points collected in advance from the
agent's belief space. We present a randomized point-based value
iteration algorithm called Perseus. The algorithm performs
approximate value backup stages, ensuring that in each backup stage
the value of each point in the belief set is improved; the key
observation is that a single backup may improve the value of many
belief points. Contrary to other point-based methods, Perseus backs
up only a (randomly selected) subset of points in the belief set,
sufficient for improving the value of each belief point in the set.
We show how the same idea can be extended to dealing with continuous
action spaces. Experimental results show the potential of Perseus in
large scale POMDP problems.

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Steven Minton
JAIR Managing Editor

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