Online Learning in Periodic Zero-Sum GamesDownload PDF

21 May 2021, 20:45 (edited 26 Oct 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: game theory, Poincar\'{e} recurrence, dynamical systems, zero-sum games, equilibrium, online learning, regret
  • TL;DR: We study online no-regret dynamics in periodic zero-sum games and evaluate the robustness of Poincar\'{e} recurrence and time-average equilibrium convergence in this class.
  • Abstract: A seminal result in game theory is von Neumann's minmax theorem, which states that zero-sum games admit an essentially unique equilibrium solution. Classical learning results build on this theorem to show that online no-regret dynamics converge to an equilibrium in a time-average sense in zero-sum games. In the past several years, a key research direction has focused on characterizing the transient behavior of such dynamics. General results in this direction show that broad classes of online learning dynamics are cyclic, and formally Poincar\'{e} recurrent, in zero-sum games. We analyze the robustness of these online learning behaviors in the case of periodic zero-sum games with a time-invariant equilibrium. This model generalizes the usual repeated game formulation while also being a realistic and natural model of a repeated competition between players that depends on exogenous environmental variations such as time-of-day effects, week-to-week trends, and seasonality. Interestingly, time-average convergence may fail even in the simplest such settings, in spite of the equilibrium being fixed. In contrast, using novel analysis methods, we show that Poincar\'{e} recurrence provably generalizes despite the complex, non-autonomous nature of these dynamical systems.
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