Reference : Perseus: Randomized point-based value iteration for POMDPs
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/11047
Perseus: Randomized point-based value iteration for POMDPs
English
Spaan, M. T. J. [> >]
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
2005
Journal of Artificial Intelligence Research
Morgan Kaufmann Publishers
24
195-220
Yes (verified by ORBilu)
1076-9757
1943-5037
San Francisco
CA
[en] 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.
http://hdl.handle.net/10993/11047
http://www.jair.org/media/1659/live-1659-2413-jair.pdf

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