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Efficiently Computing Nash Equilibria in Adversarial Team Markov Games
In this work, we depart from those prior results by investigating infinite-horizon \emph{adversarial team Markov games}, a natural and well-motivated class of games in which a team of identically-interested players -- in the absence of any explicit coordination or communication -- is competing against an adversarial player.
Towards convergence to Nash equilibria in two-team zero-sum games
On a brighter note, we propose a first-order method that leverages control theory techniques and under some conditions enjoys last-iterate local convergence to a Nash equilibrium.
Teamwork makes von Neumann work:Min-Max Optimization in Two-Team Zero-Sum Games
Motivated by recent advances in both theoretical and applied aspects of multiplayer games, spanning from e-sports to multi-agent generative adversarial networks, we focus on min-max optimization in team zero-sum games.
