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Temporal logic control of general Markov decision processes by approximate policy refinement

Lookup NU author(s): Dr Sadegh SoudjaniORCiD



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


The formal verification and controller synthesis for general Markov decision processes (gMDPs) that evolve over uncountable state spaces are computationally hard and thus generally rely on the use of approximate abstractions. In this paper, we contribute to the state of the art of control synthesis for temporal logic properties by computing and quantifying a less conservative gridding of the continuous state space of linear stochastic dynamic systems and by giving a new approach for control synthesis and verification that is robust to the incurred approximation errors. The approximation errors are expressed as both deviations in the outputs of the gMDPs and in the probabilistic transitions.

Publication metadata

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

Editor(s): Alessandro Abate, Antoine Girard, Maurice Heemels

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 6th IFAC Conference on Analysis and Design of Hybrid Systems (ADHS 2018)

Year of Conference: 2018

Pages: 73-78

Online publication date: 31/08/2018

Acceptance date: 01/03/2018

Date deposited: 04/11/2019

ISSN: 2405-8963

Publisher: International Federation of Automatic Control


DOI: 10.1016/j.ifacol.2018.08.013

Series Title: IFAC-PapersOnline