Reference : Perseus: Randomized point-based value iteration for POMDPs
Scientific journals : Article
Engineering, computing & technology : Computer science
Perseus: Randomized point-based value iteration for POMDPs
Spaan, M. T. J. [> >]
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Journal of Artificial Intelligence Research
Morgan Kaufmann Publishers
Yes (verified by ORBilu)
San Francisco
[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.

File(s) associated to this reference

Fulltext file(s):

Open access
download.pdf postprint1.32 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.