Reaching consensus and convergence to equilibrium are two major challenges of multi-
agent systems. Although each has attracted significant attention, relatively few studies …

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 view of the complexity of the dynamics of learning in games, we seek to decompose a
game into simpler components where the dynamics' long-run behavior is well understood. A …

Recent advances in quantum computing and in particular, the introduction of quantum
GANs, have led to increased interest in quantum zero-sum game theory, extending the …

Arguably one of the thorniest problems in game theory is that of equilibrium selection.
Specifically, in the presence of multiple equilibria do self-interested learning dynamics …

Although learning has found wide application in multi-agent systems, its effects on the
temporal evolution of a system are far from understood. This paper focuses on the dynamics …

Adversarial multiplayer games are an important object of study in multiagent learning. In
particular, polymatrix zero-sum games are a multiplayer setting where Nash equilibria are …

The predominant paradigm in evolutionary game theory and more generally online learning
in games is based on a clear distinction between a population of dynamic agents that …

Gradient-based optimization has been critical to the success of machine learning, updating
a single set of parameters to minimize a single loss. A growing number of applications rely …