NDLI: Region-based value iteration for partially observable Markov decision processes

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Region-based value iteration for partially observable Markov decision processes

Content Provider	ACM Digital Library
Author	Li, Hui Carin, Lawrence Liao, Xuejun
Abstract	An approximate region-based value iteration (RBVI) algorithm is proposed to find the optimal policy for a partially observable Markov decision process (POMDP). The proposed RBVI approximates the true polyhedral partition of the belief simplex with an ellipsoidal partition, such that the optimal value function is linear in each of the ellipsoidal regions. The position and shape of each region, as well as the gradient (alpha-vector) of the optimal value function in the region, are parameterized explicitly, and are estimated via efficient expectation maximization (EM) and variational Bayesian EM (VBEM), based on a set of selected sample belief points. The RBVI maintains a much smaller number of alpha-vectors than point-based methods and yields a more parsimonious representation that approximates the true value function in the maximum likelihood (ML) sense. The results on benchmark problems show that the proposed RBVI is comparable in performance to state-of-the-art algorithms, despite of the small number of alpha-vectors that are used.
Starting Page	561
Ending Page	568
Page Count	8
File Format	PDF
ISBN	1595933832
DOI	10.1145/1143844.1143915
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-06-25
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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