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

Exploration-exploitation is a powerful and practical tool in multi-agent learning (MAL);
however, its effects are far from understood. To make progress in this direction, we study a …

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 …

Routing games are amongst the most well studied domains of game theory. How relevant
are these pen-and-paper calculations to understanding the reality of everyday traffic …

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 …

Ridge Rider (RR) is an algorithm for finding diverse solutions to optimization problems by
following eigenvectors of the Hessian (" ridges"). RR is designed for conservative gradient …

Understanding the impact of exploration on the behaviour of multi-agent learning has, so far,
benefited from the restriction to potential, or network zero-sum games in which convergence …

In multi-agent environments in which coordination is desirable, the history of play often
causes lock-in at sub-optimal outcomes. Notoriously, technologies with significant …

The well-known replicator equation in evolutionary game theory describes how population-
level behaviors change over time when individuals make decisions using simple imitation …

Modelling the dynamics of multi-agent reinforcement learning has long been an important
research topic. Most of the previous works focus on agents learning under global …