This paper examines the convergence of no-regret learning in games with continuous action
sets. For concreteness, we focus on learning via “dual averaging”, a widely used class of no …

Understanding the behavior of no-regret dynamics in general N-player games is a
fundamental question in online learning and game theory. A folk result in the field states that …

Understanding the behavior of no-regret dynamics in general 𝑁-player games is a
fundamental question in online learning and game theory. A folk result in the field states that …

In this work, we propose introduce a variant of online stochastic gradient descent and prove
it converges to Nash equilibria and simultaneously it has sublinear regret for the class of …

We introduce an online learning algorithm in the bandit feedback model that, once adopted
by all agents of a congestion game, results in game-dynamics that converge to an $\epsilon …

We study opinion formation games based on the Friedkin-Johnsen (FJ) model. We are
interested in simple and natural variants of the FJ model that use limited information …

In this paper, we provide exponential rates of convergence to the interior Nash equilibrium
for continuous-time dual-space game dynamics such as mirror descent (MD) and actor-critic …

Gradient-based algorithms have shown great promise in solving large (two-player) zero-sum
games. However, their success has been mostly confined to the low-precision regime since …

A natural goal in multiagent learning besides finding equilibria is to learn rationalizable
behavior, where players learn to avoid iteratively dominated actions. However, even in the …

Dominated actions are natural (and perhaps the simplest possible) multi-agent
generalizations of sub-optimal actions as in standard single-agent decision making. Thus …