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Reinforcement learning in a prisoner's dilemma

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  • Dolgopolov, Arthur

Abstract

I characterize the outcomes of a class of model-free reinforcement learning algorithms, such as stateless Q-learning, in a prisoner's dilemma. The behavior is studied in the limit as players stop experimenting after sufficiently exploring their options. A closed form relationship between the learning rate and game payoffs reveals whether the players will learn to cooperate or defect. The findings have implications for algorithmic collusion and also apply to asymmetric learners with different experimentation rules.

Suggested Citation

  • Dolgopolov, Arthur, 2024. "Reinforcement learning in a prisoner's dilemma," Games and Economic Behavior, Elsevier, vol. 144(C), pages 84-103.
    • Handle: RePEc:eee:gamebe:v:144:y:2024:i:c:p:84-103
    DOI: 10.1016/j.geb.2024.01.004
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    References listed on IDEAS

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    Cited by:

    1. Zhang Xu & Wei Zhao, 2024. "On Mechanism Underlying Algorithmic Collusion," Papers 2409.01147, arXiv.org.

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    More about this item

    Keywords

    Q-learning; Stochastic stability; Evolutionary game theory; Collusion; Pricing-algorithms;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices

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