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Keywords:
stochastic games; optimal control; potential approach; dynamic programming
Summary:
In this paper, we study the problem of finding deterministic (also known as feedback or closed-loop) Markov Nash equilibria for a class of discrete-time stochastic games. In order to establish our results, we develop a potential game approach based on the dynamic programming technique. The identified potential stochastic games have Borel state and action spaces and possibly unbounded nondifferentiable cost-per-stage functions. In particular, the team (or coordination) stochastic games and the stochastic games with an action independent transition law are covered.
References:
[1] Dragone, D., Lambertini, L., Leitmann, G., Palestini, A.: Hamiltonian potential functions for differential games. Automatica 62 (2015), 134-138. DOI  | MR 3423980
[2] Fleming, W. H., Rishel, R. W.: Deterministic and stochastic optimal control. Springer Science and Business Media 1 (2012). MR 0454768
[3] Fonseca-Morales, A., Hernández-Lerma, O.: Potential differential games. Dyn. Games Appl. 8 (2018), 254-279. DOI  | MR 3784963
[4] Fonseca-Morales, A., Hernández-Lerma, O.: Stochastic differential games: the potential approach. Stochastics 92, (2020), 1125-1138. DOI  | MR 4156004
[5] González-Sánchez, D., Hernández-Lerma, O.: Discrete-time Stochastic Control and Dynamic Potential Games: The Euler-equation Approach. Springer, New York 2013. MR 3114623
[6] Gopalakrishnan, R., Marden, J.Ŕ., Wierman, A.: Potential games are necessary to ensure pure Nash equilibria in cost sharing games. Math. Oper. Res. 39 (2014), 1252-1296. DOI  | MR 3279766
[7] Hernández-Lerma, O., Lasserre, J. B.: Discrete-time Markov Control Processes: Basic Optimality Criteria. Springer-Verlag, New York 1996. MR 1363487
[8] Hernández-Lerma, O., Lasserre, J. B.: Further Topics on Discrete-Time Markov Control Processes. Springer-Verlag, New York 1999. MR 1697198 | Zbl 0928.93002
[9] Luque-Vázquez, F., Minjárez-Sosa, J. A.: Empirical approximation in Markov games under unbounded payoff: discounted and average criteria. Kybernetika 53 (2017), 4, 694-716. DOI  | MR 3730259
[10] Mazalov, V. V., Rettieva, A. N., Avrachenkov, K. E.: Linear-quadratic discrete-time dynamic potential games. Autom. Remote Control 78 (2017), 1537-1544. DOI  | MR 3702566
[11] Mguni, D.: Stochastic potential games.
[12] Minjárez-Sosa, J. A.: Zero-Sum Discrete-Time Markov Games with Unknown Disturbance Distribution: Discounted and Average Criteria. Springer Nature, Cham 2020. DOI  | MR 4292281
[13] Monderer, D., Shapley, L. S.: Potential games. Game Econ. Behav. 14 (1996), 124-143. DOI  | MR 1393599
[14] Potters, J. A. M., Raghavan, T. E. S., Tijs, S. H.: Pure equilibrium strategies for stochastic games via potential functions. Adv. Dyn. Games Appl. Birkhauser, Boston 2009, pp. 433-444. DOI  | MR 2521681
[15] Robles-Aguilar, A. D., González-Sánchez, D., Minjárez-Sosa, J. A.: Estimation of equilibria in an advertising game with unknown distribution of the response to advertising efforts. In: Modern Trends in Controlled Stochastic Processes, Theory and Applications, V.III, (A. Piunovskiy and Y. Zhang Eds.), Springer Nature, Cham 2021. pp. 148-165. DOI  | MR 4437149
[16] Rosenthal, R. W.: A class of games possessing pure-strategy Nash equilibria. Int. J. Game Theory 2 (1973), 65-67. DOI  | MR 0319584
[17] Slade, E. M.: What does an oligopoly maximize?. J. Ind. Econ. 42 (1994), 45-61. DOI 
[18] Macua, S. V., Zazo, S., Zazo, J.: Learning parametric closed-Loop policies for Markov potential games.
[19] Zazo, S., Zazo, J., Sánchez-Fernández, M.: A control theoretic approach to solve a constrained uplink power dynamic game. In: IEEE, 22nd European Signal Processing Conference on (EUSIPCO) 2014, pp. 401-405.
[20] Zazo, S., Valcarcel, S., Sánchez-Fernández, M., Zazo, J.: Dynamic potential games with constraints: fundamentals and applications in communications. IEEE Trans. Signal Proc. 64 (2016), 3806-3821. DOI  | MR 3515718
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