Contributed talk: Characterizing Equilibria in Stackelberg Games

Dec 14, 2019



This paper investigates the convergence of learning dynamics in Stackelberg games on continuous action spaces, a class of games distinguished by the hierarchical order of play between agents. We establish connections between the Nash and Stackelberg equilibrium concepts and characterize conditions under which attractors of simultaneous gradient descent are Stackelberg equilibria in zero-sum games. Moreover, we show that the only stable attractors of the Stackelberg gradient dynamics are Stackelberg equilibria in zero-sum games. Using this insight, we develop two-timescale learning dynamics that converge to Stackelberg equilibria in zero-sum games and the set of stable attractors in general-sum games.



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Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.

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