Dynamic programming for POMDP with jointly discrete and continuous state-spaces

Donghwan Lee, Niao He, Jianghai Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work, we study dynamic programming (DP) algorithms for partially observable Markov decision processes with jointly continuous and discrete state-spaces. We consider a class of stochastic systems which have coupled discrete and continuous systems, where only the continuous state is observable. Such a family of systems includes many realworld systems, for example, Markovian jump linear systems and physical systems interacting with humans. A finite history of observations is used as a new information state, and the convergence of the corresponding DP algorithms is proved. In particular, we prove that the DP iterations converge to a certain bounded set around an optimal solution. Although deterministic DP algorithms are studied in this paper, it is expected that this fundamental work lays foundations for advanced studies on reinforcement learning algorithms under the same family of systems.

Original languageEnglish (US)
Title of host publication2019 American Control Conference, ACC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1250-1255
Number of pages6
ISBN (Electronic)9781538679265
DOIs
StatePublished - Jul 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: Jul 10 2019Jul 12 2019

Publication series

NameProceedings of the American Control Conference
Volume2019-July
ISSN (Print)0743-1619

Conference

Conference2019 American Control Conference, ACC 2019
CountryUnited States
CityPhiladelphia
Period7/10/197/12/19

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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  • Cite this

    Lee, D., He, N., & Hu, J. (2019). Dynamic programming for POMDP with jointly discrete and continuous state-spaces. In 2019 American Control Conference, ACC 2019 (pp. 1250-1255). [8815313] (Proceedings of the American Control Conference; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/acc.2019.8815313