Prediction-Directed Compression of POMDPs

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Prediction-Directed Compression of POMDPs
High dimensionality of belief space in Partially Observable Markov Decision Processes (POMDPs) is one of the major causes that severely restricts the applicability of this model. Previous studies have demonstrated that the dimensionality of a POMDP can eventually be reduced by transforming it into an equivalent Predictive State Representation (PSR). In this paper, we address the problem of finding an approximate and compact PSR model corresponding to a given POMDP model. We formulate this problem in an optimization framework. Our algorithm tries to minimize the potential error that missing some core tests may cause. We also present an empirical evaluation on benchmark problems, illustrating the performance of this approach.
Abdeslam Boularias, Masoumeh T. Izadi, Brahim Chai
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Authors Abdeslam Boularias, Masoumeh T. Izadi, Brahim Chaib-draa
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