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Prediction-Directed Compression of POMDPs

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83823

Boularias,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Boularias, A., Izadi, M., & Chaib-Draa, B. (2008). Prediction-Directed Compression of POMDPs. Proceedings of the Seventh International Conference on Machine Learning and Applications (ICMLA 2008), 99-105.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C639-8
Zusammenfassung
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.