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October  2017, 4(4): 369-383. doi: 10.3934/jdg.2017020

A probability criterion for zero-sum stochastic games

Xiangxiang Huang 1, , Xianping Guo a,, and  Jianping Peng b,

1. 

School of Computer Science and Network Security, Dongguan University of Technology, Dongguan, 523808, China
a. 

School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, China
b. 

Sun Yat-sen Business School, Sun Yat-Sen University, Guangzhou, 510275, China

* Corresponding author: Xianping Guo

Received  April 2017 Revised  July 2017 Published  October 2017

This paper introduces a probability criterion for two-person zero-sum stochastic games, and focuses on the probability that the payoff before the first passage time to some target state set exceeds a level formulated by both players, which shows the security for player 1 and the risk for player 2. For the game model based on discrete-time Markov chains, under a suitable condition on the game's primitive data, we establish the Shapley equation, from which the existences of the value of the game and a pair of optimal policies are ensured. We also provide a recursive way of computing (or at least approximating) the value of the game. At last, the application of our main result is exhibited via an inventory system.

Keywords: Zero-sum game, discrete-time Markov chain, probability criterion, first passage time, a pair of optimal policies.
Mathematics Subject Classification: Primary: 91A60; Secondary: 60J27.
Citation: Xiangxiang Huang, Xianping Guo, Jianping Peng. A probability criterion for zero-sum stochastic games. Journal of Dynamics & Games, 2017, 4 (4) : 369-383. doi: 10.3934/jdg.2017020
References:
[1]

K. Fan, Minimax theorems, Proc. Nat. Acad. Sci., 39 (1953), 42-47.  doi: 10.1073/pnas.39.1.42.  Google Scholar

[2]

E. A. Feinberg and J. Fei, An inequality for variances of the discounted rewards, J. Appl. Probab., 46 (2009), 1209-1212.  doi: 10.1017/S0021900200006240.  Google Scholar

[3]

X. P. Guo and O. Hernández-Lerma, Zero-sum games for continuous-time Markov chains with unbounded transition and average payoff rates, J. Appl. Probab., 40 (2003), 327-345.  doi: 10.1017/S0021900200019331.  Google Scholar

[4]

X.P. Guo and O. Hernández-Lerma, Nonzero-sum games for continuous-time Markov chains with unbounded discounted payoffs, J. Appl. Probab., 42 (2005), 303-320.  doi: 10.1017/S002190020000036X.  Google Scholar

[5]

X. P. Guo and O. Hernández-Lerma, Zero-sum games for continuous-time jump Markov processes in Polish spaces: discounted payoffs, Adv. in Appl. Probab., 39 (2007), 645-668.  doi: 10.1017/S0001867800001981.  Google Scholar

[6]

X. P. Guo and O. Hernández-Lerma, Continuous-Time Markov Decision Processes: Theory and Applications, Springer-Verlag, Berlin, 2009. doi: 10.1007/978-3-642-02547-1.  Google Scholar

[7]

X. P. Guo, M. Vykertas and Y. Zhang, Absorbing continuous-time Markov decision processes with total cost criteria, Adv. in Appl. Probab., 45 (2013), 490-519.  doi: 10.1017/S0001867800006418.  Google Scholar

[8]

O. Hernández-Lerma and J. B. Lasserre, Zero-sum stochastic games in Borel spaces: average payoff criterion, SIAM J. Control Optim., 39 (2000), 1520-1539.  doi: 10.1137/S0363012999361962.  Google Scholar

[9]

O. Hernández-Lerma and J. B. Lasserre, Discrete-time Markov Control Processes: Basic Optimality Criteria, Springer-Verlag, New York, 1996. doi: 10.1007/978-1-4612-0729-0.  Google Scholar

[10]

O. Hernández-Lerma and J. B. Lasserre, Further Topics on Discrete-Time Markov Control Processes, Springer-Verlag, New York, 1999. doi: 10.1007/978-1-4612-0561-6.  Google Scholar

[11]

Y. H. Huang, X. P. Guo and X. Y. Song, Performance analysis for controlled semi-Markov systems with application to maintenance, J. Optim. Theory Appl., 150 (2011), 395-415.  doi: 10.1007/s10957-011-9813-7.  Google Scholar

[12]

Y. H. Huang, X. P. Guo and Z. F. Li, Minimum risk probability for finite horizon semi-Markov decision processes, J. Math. Anal. Appl., 402 (2013), 378-391.  doi: 10.1016/j.jmaa.2013.01.021.  Google Scholar

[13]

A. Jaśkiewicz and A. S. Nowak, Zero-sum ergodic stochastic games with Feller transition probabilities, SIAM J. Control Optim., 45 (2006), 773-789.  doi: 10.1137/S0363012904443257.  Google Scholar

[14]

A. Jaśkiewicz, Zero-sum ergodic semi-Markov games with weakly continuous transition probabilities, J. Optim. Theory Appl., 141 (2009), 321-347.  doi: 10.1007/s10957-008-9491-2.  Google Scholar

[15]

A. Jaśkiewicz and A. S. Nowak, Non-Zero-Sum Stochastic Games, In: Basar T, Zaccour G (eds.) Handbook of Dynamic Games, 2016. Google Scholar

[16]

A. S. Nowak, Optimal strategies in a class of zero-sum ergodic stochastic games, Math. Methods Oper. Res., 50 (1999), 399-419.  doi: 10.1007/s001860050078.  Google Scholar

[17]

A. S. Nowak, Measurable selection theorems for minimax stochastic optimization problems, SIAM J.Control Optim., 23 (1985), 466-476.  doi: 10.1137/0323030.  Google Scholar

[18]

Y. Ohtsubo, Minimizing risk models in stochastic shortest path problems, Math. Methods Oper. Res., 57 (2003), 79-88.  doi: 10.1007/s001860200246.  Google Scholar

[19]

Y. Ohtsubo, Optimal threshold probability in undiscounted Markov decision processes with a target set, Appl. Math. Comput., 149 (2004), 519-532.  doi: 10.1016/S0096-3003(03)00158-9.  Google Scholar

[20]

T. Parthasarathy and S. Sinha, Existence of equilibrium stationary strategies in nonzero-sum discounted stochastic games with uncountable state space and state independent transitions, Internat. J. Game Theory, 18 (1989), 189-194.  doi: 10.1007/BF01268158.  Google Scholar

[21]

M. L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, John Wiley & Sons, Inc., New York, 1994.  Google Scholar

[22]

S. Saha, Zero-sum stochastic games with partial information and average payoff, J. Optim. Theory Appl., 160 (2014), 344-354.  doi: 10.1007/s10957-013-0359-8.  Google Scholar

[23]

M. Sakaguchi and Y. Ohtsubo, Optimal threshold probability and expectation in semi-Markov decision processes, Appl. Math. Comput., 216 (2010), 2947-2958.  doi: 10.1016/j.amc.2010.04.007.  Google Scholar

[24]

L. I. Sennott, Nonzero-sum stochastic games with unbounded costs: discounted and average cost cases, Z. Oper. Res., 40 (1994), 145-162.  doi: 10.1007/BF01432807.  Google Scholar

[25]

O. Vega-Amaya, Zero-sum average semi-Markov games: fixed-point solutions of the Shapley equation, SIAM J. Control Optim., 42 (2003), 1876-1894.  doi: 10.1137/S0363012902408423.  Google Scholar

show all references

References:
[1]

K. Fan, Minimax theorems, Proc. Nat. Acad. Sci., 39 (1953), 42-47.  doi: 10.1073/pnas.39.1.42.  Google Scholar

[2]

E. A. Feinberg and J. Fei, An inequality for variances of the discounted rewards, J. Appl. Probab., 46 (2009), 1209-1212.  doi: 10.1017/S0021900200006240.  Google Scholar

[3]

X. P. Guo and O. Hernández-Lerma, Zero-sum games for continuous-time Markov chains with unbounded transition and average payoff rates, J. Appl. Probab., 40 (2003), 327-345.  doi: 10.1017/S0021900200019331.  Google Scholar

[4]

X.P. Guo and O. Hernández-Lerma, Nonzero-sum games for continuous-time Markov chains with unbounded discounted payoffs, J. Appl. Probab., 42 (2005), 303-320.  doi: 10.1017/S002190020000036X.  Google Scholar

[5]

X. P. Guo and O. Hernández-Lerma, Zero-sum games for continuous-time jump Markov processes in Polish spaces: discounted payoffs, Adv. in Appl. Probab., 39 (2007), 645-668.  doi: 10.1017/S0001867800001981.  Google Scholar

[6]

X. P. Guo and O. Hernández-Lerma, Continuous-Time Markov Decision Processes: Theory and Applications, Springer-Verlag, Berlin, 2009. doi: 10.1007/978-3-642-02547-1.  Google Scholar

[7]

X. P. Guo, M. Vykertas and Y. Zhang, Absorbing continuous-time Markov decision processes with total cost criteria, Adv. in Appl. Probab., 45 (2013), 490-519.  doi: 10.1017/S0001867800006418.  Google Scholar

[8]

O. Hernández-Lerma and J. B. Lasserre, Zero-sum stochastic games in Borel spaces: average payoff criterion, SIAM J. Control Optim., 39 (2000), 1520-1539.  doi: 10.1137/S0363012999361962.  Google Scholar

[9]

O. Hernández-Lerma and J. B. Lasserre, Discrete-time Markov Control Processes: Basic Optimality Criteria, Springer-Verlag, New York, 1996. doi: 10.1007/978-1-4612-0729-0.  Google Scholar

[10]

O. Hernández-Lerma and J. B. Lasserre, Further Topics on Discrete-Time Markov Control Processes, Springer-Verlag, New York, 1999. doi: 10.1007/978-1-4612-0561-6.  Google Scholar

[11]

Y. H. Huang, X. P. Guo and X. Y. Song, Performance analysis for controlled semi-Markov systems with application to maintenance, J. Optim. Theory Appl., 150 (2011), 395-415.  doi: 10.1007/s10957-011-9813-7.  Google Scholar

[12]

Y. H. Huang, X. P. Guo and Z. F. Li, Minimum risk probability for finite horizon semi-Markov decision processes, J. Math. Anal. Appl., 402 (2013), 378-391.  doi: 10.1016/j.jmaa.2013.01.021.  Google Scholar

[13]

A. Jaśkiewicz and A. S. Nowak, Zero-sum ergodic stochastic games with Feller transition probabilities, SIAM J. Control Optim., 45 (2006), 773-789.  doi: 10.1137/S0363012904443257.  Google Scholar

[14]

A. Jaśkiewicz, Zero-sum ergodic semi-Markov games with weakly continuous transition probabilities, J. Optim. Theory Appl., 141 (2009), 321-347.  doi: 10.1007/s10957-008-9491-2.  Google Scholar

[15]

A. Jaśkiewicz and A. S. Nowak, Non-Zero-Sum Stochastic Games, In: Basar T, Zaccour G (eds.) Handbook of Dynamic Games, 2016. Google Scholar

[16]

A. S. Nowak, Optimal strategies in a class of zero-sum ergodic stochastic games, Math. Methods Oper. Res., 50 (1999), 399-419.  doi: 10.1007/s001860050078.  Google Scholar

[17]

A. S. Nowak, Measurable selection theorems for minimax stochastic optimization problems, SIAM J.Control Optim., 23 (1985), 466-476.  doi: 10.1137/0323030.  Google Scholar

[18]

Y. Ohtsubo, Minimizing risk models in stochastic shortest path problems, Math. Methods Oper. Res., 57 (2003), 79-88.  doi: 10.1007/s001860200246.  Google Scholar

[19]

Y. Ohtsubo, Optimal threshold probability in undiscounted Markov decision processes with a target set, Appl. Math. Comput., 149 (2004), 519-532.  doi: 10.1016/S0096-3003(03)00158-9.  Google Scholar

[20]

T. Parthasarathy and S. Sinha, Existence of equilibrium stationary strategies in nonzero-sum discounted stochastic games with uncountable state space and state independent transitions, Internat. J. Game Theory, 18 (1989), 189-194.  doi: 10.1007/BF01268158.  Google Scholar

[21]

M. L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, John Wiley & Sons, Inc., New York, 1994.  Google Scholar

[22]

S. Saha, Zero-sum stochastic games with partial information and average payoff, J. Optim. Theory Appl., 160 (2014), 344-354.  doi: 10.1007/s10957-013-0359-8.  Google Scholar

[23]

M. Sakaguchi and Y. Ohtsubo, Optimal threshold probability and expectation in semi-Markov decision processes, Appl. Math. Comput., 216 (2010), 2947-2958.  doi: 10.1016/j.amc.2010.04.007.  Google Scholar

[24]

L. I. Sennott, Nonzero-sum stochastic games with unbounded costs: discounted and average cost cases, Z. Oper. Res., 40 (1994), 145-162.  doi: 10.1007/BF01432807.  Google Scholar

[25]

O. Vega-Amaya, Zero-sum average semi-Markov games: fixed-point solutions of the Shapley equation, SIAM J. Control Optim., 42 (2003), 1876-1894.  doi: 10.1137/S0363012902408423.  Google Scholar

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