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International Conference on Tools and Algorithms for the Construction and Analysis of Systems

TACAS 2020: Tools and Algorithms for the Construction and Analysis of Systems pp 346–364Cite as

Simple Strategies in Multi-Objective MDPs

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12078)

Abstract

We consider the verification of multiple expected reward objectives at once on Markov decision processes (MDPs). This enables a trade-off analysis among multiple objectives by obtaining a Pareto front. We focus on strategies that are easy to employ and implement. That is, strategies that are pure (no randomization) and have bounded memory. We show that checking whether a point is achievable by a pure stationary strategy is NP-complete, even for two objectives, and we provide an MILP encoding to solve the corresponding problem. The bounded memory case is treated by a product construction. Experimental results using Storm and Gurobi show the feasibility of our algorithms.

currently affiliated with Vrije Universiteit Brussel.

Research partially supported by F.R.S.-FNRS Grant n\(^{\circ }\) F.4520.18 (ManySynth). Mickael Randour is an F.R.S.-FNRS Research Associate.

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Authors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Florent Delgrange, Joost-Pieter Katoen & Tim Quatmann

  2. UMONS – Université de Mons, Mons, Belgium

    Florent Delgrange & Mickael Randour

Authors
  1. Florent Delgrange
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  2. Joost-Pieter Katoen
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  3. Tim Quatmann
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  4. Mickael Randour
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Correspondence to Tim Quatmann .

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Editors and Affiliations

  1. Johannes Kepler University, Linz, Austria

    Prof. Armin Biere

  2. University of Birmingham, Birmingham, UK

    Prof. David Parker

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Delgrange, F., Katoen, JP., Quatmann, T., Randour, M. (2020). Simple Strategies in Multi-Objective MDPs. In: Biere, A., Parker, D. (eds) Tools and Algorithms for the Construction and Analysis of Systems. TACAS 2020. Lecture Notes in Computer Science(), vol 12078. Springer, Cham. https://doi.org/10.1007/978-3-030-45190-5_19

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