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 …

Ex ante correlation is becoming the mainstream approach for sequential adversarial team
games, where a team of players faces another team in a zero-sum game. It is known that …

We provide, to the best of our knowledge, the first computational study of extensive-form
adversarial team games. These games are sequential, zero-sum games in which a team of …

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 …

Recent milestones in equilibrium computation, such as the success of Libratus, show that it
is possible to compute strong solutions to two-player zero-sum games in theory and practice …

We focus on the problem of finding an optimal strategy for a team of players that faces an
opponent in an imperfect-information zero-sum extensive-form game. Team members are …

Despite the many recent practical and theoretical breakthroughs in computational game
theory, equilibrium finding in extensive-form team games remains a significant challenge …

We study the problem of learning a Nash equilibrium (NE) in Markov games which is a
cornerstone in multi-agent reinforcement learning (MARL). In particular, we focus on infinite …

Adversarial team games model multiplayer strategic interactions in which a team of
identically-interested players is competing against an adversarial player in a zero-sum …

In this paper, we study the class of games known as hidden-role games in which players are
assigned privately to teams and are faced with the challenge of recognizing and cooperating …