Game theory studies the mathematical models for self-interested individuals. Nash
equilibrium is arguably the most central solution in game theory. While finding the Nash …

Last-iterate convergence has received extensive study in two player zero-sum games
starting from bilinear, convex-concave up to settings that satisfy the MVI condition. Typical …

We consider online learning in multi-player smooth monotone games. Existing algorithms
have limitations such as (1) being only applicable to strongly monotone games;(2) lacking …

This paper studies a dynamic discrete-time queuing model where at every period players
get a new job and must send all their jobs to a queue that has a limited capacity. Players …

Learning in zero-sum games studies a situation where multiple agents competitively learn
their strategy. In such multi-agent learning, we often see that the strategies cycle around …

In its most basic form, a Stackelberg game is a two-player game in which a leader commits
to a (mixed) strategy, and a follower best-responds. Stackelberg games are perhaps one of …

In this paper, we consider a learning problem among non-cooperative agents interacting in
a time-varying system. Specifically, we focus on repeated linear quadratic network games, in …

This paper addresses the question of whether or not uncoupled online learning algorithms
converge to the Nash equilibrium in pricing competition or whether they can learn to collude …

Last-iterate behaviors of learning algorithms in repeated two-player zero-sum games have
been extensively studied due to their wide applications in machine learning and related …

In this paper, we present a framework for multi-agent learning in a nonstationary dynamic
network environment. More specifically, we examine projected gradient play in smooth …