A unique challenge in Multi-Agent Reinforcement Learning (MARL) is the\emph {curse of
multiagency}, where the description length of the game as well as the complexity of many …

Abstract Policy-Space Response Oracles (PSRO) is an influential algorithm framework for
approximating a Nash Equilibrium (NE) in multi-agent non-transitive games. Many previous …

This paper considers the challenging tasks of Multi-Agent Reinforcement Learning (MARL)
under partial observability, where each agent only sees her own individual observations and …

In many practical applications, such as poker, chess, drug interdiction, cybersecurity, and
national defense, players often have adversarial stances, ie, the selfish actions of each …

A conceptually appealing approach for learning Extensive-Form Games (EFGs) is to convert
them to Normal-Form Games (NFGs). This approach enables us to directly translate state-of …

Partial Observability--where agents can only observe partial information about the true
underlying state of the system--is ubiquitous in real-world applications of Reinforcement …

In this paper, we establish efficient and uncoupled learning dynamics so that, when
employed by all players in multiplayer perfect-recall imperfect-information extensive-form …

Abstract Imperfect-Information Extensive-Form Games (IIEFGs) is a prevalent model for real-
world games involving imperfect information and sequential plays. The Extensive-Form …

One of the most popular methods for learning Nash equilibrium (NE) in large-scale imperfect
information extensive-form games (IIEFGs) is the neural variants of counterfactual regret …

Imperfect information games (IIG) are games in which each player only partially observes
the current game state. We study how to learn $\epsilon $-optimal strategies in a zero-sum …