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Verification of General Markov Decision Processes by Approximate Similarity Relations and Policy Refinement

Lookup NU author(s): Dr Sadegh SoudjaniORCiD



This is the authors' accepted manuscript of an article that has been published in its final definitive form by Society for Industrial and Applied Mathematics, 2017.

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In this work we introduce new approximate similarity relations that are shown to be key for policy (or control) synthesis over general Markov decision processes. The models of interest are discrete-time Markov decision processes, endowed with uncountably-infinite state spaces and metric output (or observation) spaces. The new relations, underpinned by the use of metrics, allow in particular for a useful trade-off between deviations over probability distributions on states, and distances between model outputs. We show that the new probabilistic similarity relations, inspired by a notion of simulation developed for finite-state models, can be effectively employed over general Markov decision processes for verification purposes, and specifically for control refinement from abstract models.

Publication metadata

Author(s): Haesaert S, Esmaeil Zadeh Soudjani S, Abate A

Publication type: Article

Publication status: Published

Journal: SIAM Journal on Control and Optimization

Year: 2017

Volume: 55

Issue: 4

Pages: 2333-2367

Online publication date: 03/08/2017

Acceptance date: 14/03/2017

Date deposited: 07/11/2019

ISSN (print): 0363-0129

ISSN (electronic): 1095-7138

Publisher: Society for Industrial and Applied Mathematics


DOI: 10.1137/16M1079397


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