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Learning in Games via Reinforcement and Regularization

Author

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  • Panayotis Mertikopoulos

    (CNRS (French National Center for Scientific Research), LIG, F-38000 Grenoble, France; and University Grenoble Alpes, LIG, F-38000 Grenoble, France)

  • William H. Sandholm

    (Department of Economics, University of Wisconsin, Madison Wisconsin 53706)

Abstract

We investigate a class of reinforcement learning dynamics where players adjust their strategies based on their actions’ cumulative payoffs over time—specifically, by playing mixed strategies that maximize their expected cumulative payoff minus a regularization term. A widely studied example is exponential reinforcement learning, a process induced by an entropic regularization term which leads mixed strategies to evolve according to the replicator dynamics. However, in contrast to the class of regularization functions used to define smooth best responses in models of stochastic fictitious play, the functions used in this paper need not be infinitely steep at the boundary of the simplex; in fact, dropping this requirement gives rise to an important dichotomy between steep and nonsteep cases. In this general framework, we extend several properties of exponential learning, including the elimination of dominated strategies, the asymptotic stability of strict Nash equilibria, and the convergence of time-averaged trajectories in zero-sum games with an interior Nash equilibrium.

Suggested Citation

  • Panayotis Mertikopoulos & William H. Sandholm, 2016. "Learning in Games via Reinforcement and Regularization," Mathematics of Operations Research, INFORMS, vol. 41(4), pages 1297-1324, November.
  • Handle: RePEc:inm:ormoor:v:41:y:2016:i:4:p:1297-1324
    DOI: 10.1287/moor.2016.0778
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Bravo, Mario & Mertikopoulos, Panayotis, 2017. "On the robustness of learning in games with stochastically perturbed payoff observations," Games and Economic Behavior, Elsevier, vol. 103(C), pages 41-66.
    2. Apostolos Burnetas, 2022. "Learning and data-driven optimization in queues with strategic customers," Queueing Systems: Theory and Applications, Springer, vol. 100(3), pages 517-519, April.
    3. Mertikopoulos, Panayotis & Sandholm, William H., 2018. "Riemannian game dynamics," Journal of Economic Theory, Elsevier, vol. 177(C), pages 315-364.
    4. Kuang Xu & Se-Young Yun, 2020. "Reinforcement with Fading Memories," Mathematics of Operations Research, INFORMS, vol. 45(4), pages 1258-1288, November.
    5. Shiva Raj Pokhrel & Carey Williamson, 2022. "A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance," Future Internet, MDPI, vol. 14(9), pages 1-23, August.
    6. Stefanos Leonardos & Iosif Sakos & Costas Courcoubetis & Georgios Piliouras, 2020. "Catastrophe by Design in Population Games: Destabilizing Wasteful Locked-in Technologies," Papers 2007.12877, arXiv.org.
    7. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.
    8. Jibang Wu & Weiran Shen & Fei Fang & Haifeng Xu, 2022. "Inverse Game Theory for Stackelberg Games: the Blessing of Bounded Rationality," Papers 2210.01380, arXiv.org.
    9. Martino Banchio & Giacomo Mantegazza, 2022. "Adaptive Algorithms and Collusion via Coupling," Papers 2202.05946, arXiv.org, revised Nov 2022.

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