Recent applications that arise in machine learning have surged significant interest in solving
min-max saddle point games. This problem has been extensively studied in the convex …

Learning from repeated play in a fixed two-player zero-sum game is a classic problem in
game theory and online learning. We consider a variant of this problem where the game …

Last-iterate convergence has received extensive study in two player zero-sum games
starting from bilinear, convex-concave up to settings that satisfy the MVI condition. Typical …

In metaheuristic algorithms, finding the optimal balance between exploration and
exploitation is a key research topic that remains open. In the nature, a reptile called Side …

In 1950, Nash proposed a natural equilibrium solution concept for games hence called Nash
equilibrium, and proved that all finite games have at least one. The proof is through a simple …

We show for the first time that it is possible to reconcile in online learning in zero-sum games
two seemingly contradictory objectives: vanishing time-average regret and non-vanishing …

Replicator dynamics, the continuous-time analogue of Multiplicative Weights Updates, is the
main dynamic in evolutionary game theory. In simple evolutionary zero-sum games, such as …

Routing games are amongst the most studied classes of games in game theory. Their most
well-known property is that learning dynamics typically converge to equilibria implying …

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 …

A seminal result in game theory is von Neumann's minmax theorem, which states that zero-
sum games admit an essentially unique equilibrium solution. Classical learning results build …