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Reinforcement Learning with Restrictions on the Action Set

Author

Listed:
  • Mario Bravo

    (USACH - Universidad de Santiago de Chile [Santiago])

  • Mathieu Faure

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

Consider a two-player normal-form game repeated over time. We introduce an adaptive learning procedure, where the players only observe their own realized payoff at each stage. We assume that agents do not know their own payoff function and have no information on the other player. Furthermore, we assume that they have restrictions on their own actions such that, at each stage, their choice is limited to a subset of their action set. We prove that the empirical distributions of play converge to the set of Nash equilibria for zero-sum and potential games, and games where one player has two actions.

Suggested Citation

  • Mario Bravo & Mathieu Faure, 2015. "Reinforcement Learning with Restrictions on the Action Set," Post-Print hal-01457301, HAL.
    • Handle: RePEc:hal:journl:hal-01457301
    DOI: 10.1137/130936488
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    References listed on IDEAS

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    Cited by:

    1. Dai Zusai, 2018. "Net gains in evolutionary dynamics: A unifying and intuitive approach to dynamic stability," Papers 1805.04898, arXiv.org, revised Oct 2023.

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