40 citations · 48 across the 8 of their papers we have counts for
8 papers
Effect of Adaptation Rate and Cost Display in a Human-AI Interaction Game
Jason T. Isa, Bohan Wu, Qirui Wang +4
As interactions between humans and AI become more prevalent, it is critical to have better predictors of human behavior in these interactions. We investigated how changes in the AI…
Consistent Conjectural Variations Equilibrium: Characterization & Stability for a Class of Continuous Games
Daniel J. Calderone, Benjamin J. Chasnov, Samuel A. Burden +1
Leveraging tools from the study of linear fractional transformations and algebraic Riccati equations, a local characterization of consistent conjectural variations equilibrium is g…
Human adaptation to adaptive machines converges to game-theoretic equilibria
Benjamin J. Chasnov, Lillian J. Ratliff, Samuel A. Burden
Adaptive machines have the potential to assist or interfere with human behavior in a range of contexts, from cognitive decision-making to physical device assistance. Therefore it i…
Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms
Liyuan Zheng, Tanner Fiez, Zane Alumbaugh +2
The hierarchical interaction between the actor and critic in actor-critic based reinforcement learning algorithms naturally lends itself to a game-theoretic interpretation. We adop…
Stability of Gradient Learning Dynamics in Continuous Games: Scalar Action Spaces
Benjamin J. Chasnov, Daniel Calderone, Behçet Açıkmeşe +2
Learning processes in games explain how players grapple with one another in seeking an equilibrium. We study a natural model of learning based on individual gradients in two-player…
Stability of Gradient Learning Dynamics in Continuous Games: Vector Action Spaces
Benjamin J. Chasnov, Daniel Calderone, Behçet Açıkmeşe +2
Towards characterizing the optimization landscape of games, this paper analyzes the stability of gradient-based dynamics near fixed points of two-player continuous games. We introd…
Convergence of Learning Dynamics in Stackelberg Games
Tanner Fiez, Benjamin Chasnov, Lillian J. Ratliff
This paper investigates the convergence of learning dynamics in Stackelberg games. In the class of games we consider, there is a hierarchical game being played between a leader and…
Convergence Analysis of Gradient-Based Learning with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings
Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar +1
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…