Reinforcement Learning, Collusion, and the Folk Theorem

Galit Askenazi-Golan, Domenico Mergoni Cecchelli and Edward Plumb

Papers from arXiv.org

Abstract: We explore the behaviour emerging from learning agents repeatedly interacting strategically for a wide range of learning dynamics that includes projected gradient, replicator and log-barrier dynamics. Going beyond the better-understood classes of potential games and zero-sum games, we consider the setting of a general repeated game with finite recall, for different forms of monitoring. We obtain a Folk Theorem-like result and characterise the set of payoff vectors that can be obtained by these dynamics, discovering a wide range of possibilities for the emergence of algorithmic collusion.

Date: 2024-11
New Economics Papers: this item is included in nep-gth and nep-mic
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