Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

Covering formulation, algorithms, and structural results, and linking theory to real-world
applications in controlled sensing (including social learning, adaptive radars and sequential …

Correlated equilibrium is formulated in a manner that does away with the dichotomy usually
perceived between the" Bayesian" and the" game-theoretic" view of the world. From the …

In this work, we develop provably efficient reinforcement learning algorithms for two-player
zero-sum Markov games with simultaneous moves. We consider a family of Markov games …

This paper addresses the problem of learning an equilibrium efficiently in general-sum
Markov games through decentralized multi-agent reinforcement learning. Given the …

The last decade has seen a steady increase in the application of concepts from
noncooperative game theory to such diverse fields as economics, political science, law …

The price of anarchy, defined as the ratio of the worst-case objective function value of a
Nash equilibrium of a game and that of an optimal outcome, quantifies the inefficiency of …

In the real world, agents or entities are in a continuous state of interactions. These
interactions lead to various types of complexity dynamics. One key difficulty in the study of …

We study the question of obtaining last-iterate convergence rates for no-regret learning
algorithms in multi-player games. We show that the optimistic gradient (OG) algorithm with a …

There has been significant recent interest in game-theoretic approaches to security, with
much of the recent research focused on utilizing the leader-follower Stackelberg game …