Nonzero-Sum Stochastic Games with Probability Criteria | Dynamic Games and Applications Skip to main content
Log in

Nonzero-Sum Stochastic Games with Probability Criteria

  • Published:
Dynamic Games and Applications Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

In this paper, we consider two-person nonzero-sum discrete-time stochastic games under the probability criterion. Taking \(\lambda \) for player 1 and \(\mu \) for player 2 as their profit goal, the two players are concerned with the probabilities that the rewards they earn before the first passage to some target state set are more than \(\lambda \) and \(\mu \), respectively. We firstly give a characterization of the probabilities, and then, under a mild condition, we show that the optimal value function for each player is the unique solution to the corresponding optimality equation by an iterative approximation, and then establish the existence of Nash equilibria. Finally, a queueing system is provided to show the application of our main result.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Altman E (1994) Flow control using the theory of zero-sum Markov games. IEEE Trans Autom Control 39:814–818

    Article  MathSciNet  Google Scholar 

  2. Bouakiz M, Kebir Y (1995) Target-level criterion in Markov decision processes. J Optim Theory Appl 86:1–15

    Article  MathSciNet  Google Scholar 

  3. Boda K, Filar JA, Lin YL, Spanjers L (2004) Stochastic target hitting time and the problem of early retirement. IEEE Trans Autom Control 49:409–419

    Article  MathSciNet  Google Scholar 

  4. Cao XR (2003) Semi-Markov decision problems and performance sensitivity analysis. IEEE Trans Autom Control 48:758–769

    Article  MathSciNet  Google Scholar 

  5. Fan K (1952) Fixed-point and minimax theorems in locally convex topological linear spaces. Proc Nat Acad Sci 38:121–126

    Article  MathSciNet  Google Scholar 

  6. Guo XP, Hernández-Lerma O (2003) Zero-sum games for continuous-time Markov chains with unbounded transition and average payoff rates. J Appl Probab 40:327–345

    Article  MathSciNet  Google Scholar 

  7. Guo XP, Hernández-Lerma O (2005) Nonzero-sum games for continuous-time Markov chains with unbounded discounted payoffs. J Appl Probab 42:303–320

    Article  MathSciNet  Google Scholar 

  8. Guo XP, Vykertas M, Zhang Y (2013) Absorbing continuous-time Markov decision processes with total cost criteria. Adv Appl Probab 45:490–519

    Article  MathSciNet  Google Scholar 

  9. Ghosh MK, Kumar KS, Pal C (2016) Zero-sum risk-sensitive stochastic games for continuous time Markov chains. Stoch Anal Appl 34:835–851

    Article  MathSciNet  Google Scholar 

  10. Huang YH, Guo XP, Song XY (2011) Performance analysis for controlled semi-Markov systems with application to maintenance. J Optim Theory Appl 150:395–415

    Article  MathSciNet  Google Scholar 

  11. Huo HF, Zou XL, Guo XP (2017) The risk probability criterion for discounted continuous-time Markov decision processes. Discrete Event Dyn Syst 27:675–699

    Article  MathSciNet  Google Scholar 

  12. Hernández-Lerma O, Lasserre JB (2001) Zero-sum stochastic games in Borel spaces: average payoff criterion. SIAM J Control Optim 39:1520–1539

    Article  Google Scholar 

  13. Hernández-Lerma O, Lasserre JB (1996) Discrete-time Markov control processes. Springer, New York

    Book  Google Scholar 

  14. Hernández-Lerma O, Lasserre JB (1999) Further topics on discrete-time Markov control processes. Springer, New York

    Book  Google Scholar 

  15. Huang XX, Guo XP, Peng JP (2017) A probability criterion for zero-sum stochastic games. J Dyn Games 4:369–383

    Article  MathSciNet  Google Scholar 

  16. Kira A, Ueno T, Fujita T (2012) Threshold probability of non-terminal type in finite horizon Markov decision processes. J Math Anal Appl 386:461–472

    Article  MathSciNet  Google Scholar 

  17. Liu QL, Huang XX (2017) Discrete-time zero-sum Markov games with first passage criteria. Optimization 66:571–587

    Article  MathSciNet  Google Scholar 

  18. Shapley LS (1953) Stochastic games. Proc Nat Acad Sci 39:1095–1100

    Article  MathSciNet  Google Scholar 

  19. Sennott LI (1994) Zero-sum stochastic games with unbounded costs: discounted and average cost cases. Zeitschrift für Oper Res 39:209–225

    MathSciNet  MATH  Google Scholar 

  20. Sakaguchi M, Ohtsubo Y (2013) Markov decision processes associated with two threshold probability criteria. J Control Appl 11:548–557

    Article  MathSciNet  Google Scholar 

  21. Sakaguchi M, Ohtsubo Y (2010) Optimal threshold probability and expectation in semi-Markov decision processes. Appl Math Comput 216:2947–2958

    MathSciNet  MATH  Google Scholar 

  22. Wu CB, Lin YL (1999) Minimizing risk models in Markov decision processes with policies depending on target values. J Math Anal Appl 231:47–67

    Article  MathSciNet  Google Scholar 

  23. White DJ (1993) Minimizing a threshold probability in discounted Markov decision processes. J Math Anal Appl 173:634–646

    Article  MathSciNet  Google Scholar 

  24. Wei QD, Chen X (2016) Stochastic games for continuous-time jump processes under finite-horizon payoff criterion. Appl Math Optim 74:273–301

    Article  MathSciNet  Google Scholar 

  25. Zhang WZ, Wang BF, Chen DW (2018) Continuous-time constrained stochastic games with average criteria. Oper Res Lett. 46:109–115

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the AE and the referees for their numerous valuable comments and suggestions that have improved this paper. This work was partially supported by the NSFC (Grant No. 11801073), the Natural Science Foundation of Guangdong Province (Grant No. 2017A030310598) and the Ministry of Education, Humanities and Social Sciences project (Grant No. 17JYJAZH011).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xianping Guo.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, X., Guo, X. Nonzero-Sum Stochastic Games with Probability Criteria. Dyn Games Appl 10, 509–527 (2020). https://doi.org/10.1007/s13235-019-00317-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13235-019-00317-z

Keywords

Mathematics Subject Classification

Navigation