Achieving convergence of multiple learning agents in general $ N $-player games is
imperative for the development of safe and reliable machine learning (ML) algorithms and …

The behaviour of multi-agent learning in competitive network games is often studied under
the assumption of zero-sum payoffs, for which convergence guarantees may be obtained …

Multi-agent learning algorithms have been shown to display complex, unstable behaviours
in a wide array of games. In fact, previous works indicate that convergent behaviours are …

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 …

The behaviour of multi-agent learning in many player games has been shown to display
complex dynamics outside of restrictive examples such as network zero-sum games. In …

Abstract Motivated by Generative Adverserial Networks, we study the computation of a Nash
equilibrium in concave network zero-sum games (NZSGs), a multiplayer generalization of …