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The Variance-Penalized Stochastic Shortest Path Problem

Authors Jakob Piribauer , Ocan Sankur , Christel Baier

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Author Details

Jakob Piribauer
  • Technische Universität Dresden, Germany
Ocan Sankur
  • Univ Rennes, Inria, CNRS, IRISA, France
Christel Baier
  • Technische Universität Dresden, Germany

Funding

This work was funded by DFG grant 389792660 as part of TRR 248, the Cluster of Excellence EXC 2050/1 (CeTI, project ID 390696704, as part of Germany’s Excellence Strategy), and DFG-projects BA-1679/11-1 and BA-1679/12-1.

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Jakob Piribauer, Ocan Sankur, and Christel Baier. The Variance-Penalized Stochastic Shortest Path Problem. In 49th International Colloquium on Automata, Languages, and Programming (ICALP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 229, pp. 129:1-129:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.ICALP.2022.129

Abstract

The stochastic shortest path problem (SSPP) asks to resolve the non-deterministic choices in a Markov decision process (MDP) such that the expected accumulated weight before reaching a target state is maximized. This paper addresses the optimization of the variance-penalized expectation (VPE) of the accumulated weight, which is a variant of the SSPP in which a multiple of the variance of accumulated weights is incurred as a penalty. It is shown that the optimal VPE in MDPs with non-negative weights as well as an optimal deterministic finite-memory scheduler can be computed in exponential space. The threshold problem whether the maximal VPE exceeds a given rational is shown to be EXPTIME-hard and to lie in NEXPTIME. Furthermore, a result of interest in its own right obtained on the way is that a variance-minimal scheduler among all expectation-optimal schedulers can be computed in polynomial time.

Subject Classification

ACM Subject Classification
  • Theory of computation → Verification by model checking
Keywords
  • Markov decision process
  • variance
  • stochastic shortest path problem

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