Recent algorithms have achieved superhuman performance at a number of two-player zero-
sum games such as poker and go. However, many real-world situations are multi-player …

Computing Nash equilibrium policies is a central problem in multi-agent reinforcement
learning that has received extensive attention both in theory and in practice. However …

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 …

We consider team zero-sum network congestion games with $ n $ agents playing against $
k $ interceptors over a graph $ G $. The agents aim to minimize their collective cost of …

After the great achievement of solving two-player zero-sum games, more and more AI
researchers focus on solving multiplayer games. To facilitate the development of designing …

Adversarial team games (ATGs) have garnered significant attention in recent years, leading
to the emergence of various solutions such as linear programming algorithms, multi-agent …

Many real-world applications involve some agents that fall into two teams, with payoffs that
are equal within the same team but of opposite sign across the opponent team. The so …

We consider the problem of computing stationary points in min-max optimization, with a
particular focus on the special case of computing Nash equilibria in (two-) team zero-sum …

Two-team zero-sum games are one of the most important paradigms in game theory. In this
paper, we focus on finding an unexploitable equilibrium in large team games. An …

A Nash equilibrium is an important solution concept in most forms of strategic interactions.
We are interested in computing Nash equilibria in Markov games. In turn, Markov games are …