Stopping Criteria for Value Iteration on Stochastic Games with Quantitative Objectives

Jan Kretinsky, Tobias Meggendorfer, Maximilian Weininger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

A classic solution technique for Markov decision processes (MDP) and stochastic games (SG) is value iteration (VI). Due to its good practical performance, this approximative approach is typically preferred over exact techniques, even though no practical bounds on the imprecision of the result could be given until recently. As a consequence, even the most used model checkers could return arbitrarily wrong results. Over the past decade, different works derived stopping criteria, indicating when the precision reaches the desired level, for various settings, in particular MDP with reachability, total reward, and mean payoff, and SG with reachability.In this paper, we provide the first stopping criteria for VI on SG with total reward and mean payoff, yielding the first anytime algorithms in these settings. To this end, we provide the solution in two flavours: First through a reduction to the MDP case and second directly on SG. The former is simpler and automatically utilizes any advances on MDP. The latter allows for more local computations, heading towards better practical efficiency.Our solution unifies the previously mentioned approaches for MDP and SG and their underlying ideas. To achieve this, we isolate objective-specific subroutines as well as identify objective-independent concepts. These structural concepts, while surprisingly simple, form the very essence of the unified solution.

Original languageEnglish
Title of host publication2023 38th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350335873
DOIs
StatePublished - 2023
Event38th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS 2023 - Boston, United States
Duration: 26 Jun 202329 Jun 2023

Publication series

NameProceedings - Symposium on Logic in Computer Science
Volume2023-June
ISSN (Print)1043-6871

Conference

Conference38th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS 2023
Country/TerritoryUnited States
CityBoston
Period26/06/2329/06/23

Keywords

  • Stochastic games
  • value iteration

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