no code implementations • ICML 2020 • Tanner Fiez, Benjamin Chasnov, Lillian Ratliff
Contemporary work on learning in continuous games has commonly overlooked the hierarchical decision-making structure present in machine learning problems formulated as games, instead treating them as simultaneous play games and adopting the Nash equilibrium solution concept.
1 code implementation • 25 Sep 2021 • Liyuan Zheng, Tanner Fiez, Zane Alumbaugh, Benjamin Chasnov, Lillian J. Ratliff
The hierarchical interaction between the actor and critic in actor-critic based reinforcement learning algorithms naturally lends itself to a game-theoretic interpretation.
1 code implementation • 4 Jun 2019 • Tanner Fiez, Benjamin Chasnov, Lillian J. Ratliff
Using this insight, we develop a gradient-based update for the leader while the follower employs a best response strategy for which each stable critical point is guaranteed to be a Stackelberg equilibrium in zero-sum games.
no code implementations • 30 May 2019 • Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar, Samuel A. Burden
Considering a class of gradient-based multi-agent learning algorithms in non-cooperative settings, we provide local convergence guarantees to a neighborhood of a stable local Nash equilibrium.