Search Results for author: Benjamin Chasnov

Found 4 papers, 2 papers with code

Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study

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.

Decision Making

Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms

1 code implementation25 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.

OpenAI Gym reinforcement-learning

Convergence of Learning Dynamics in Stackelberg Games

1 code implementation4 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.

Convergence Analysis of Gradient-Based Learning with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings

no code implementations30 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.

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