Despite its important applications in Machine Learning, min-max optimization of objective
functions that are nonconvex-nonconcave remains elusive. Not only are there no known first …

Multi-agent reinforcement learning has made substantial empirical progresses in solving
games with a large number of players. However, theoretically, the best known sample …

We show that computing approximate stationary Markov coarse correlated equilibria (CCE)
in general-sum stochastic games is PPAD-hard, even when there are two players, the game …

Nash equilibrium is one of the most influential solution concepts in game theory. With the
development of computer science and artificial intelligence, there is an increasing demand …

Computer science and economics have engaged in a lively interaction over the past fifteen
years, resulting in the new field of algorithmic game theory. Many problems that are central …

Multi-agent reinforcement learning (MARL) algorithms often suffer from an exponential
sample complexity dependence on the number of agents, a phenomenon known as the …

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 …

Most existing results about last-iterate convergence of learning dynamics are limited to two-
player zero-sum games, and only apply under rigid assumptions about what dynamics the …

Recently, Daskalakis, Fishelson, and Golowich (DFG)(NeurIPS '21) showed that if all agents
in a multi-player general-sum normal-form game employ Optimistic Multiplicative Weights …

We consider a monopolist seller with n heterogeneous items, facing a single buyer. The
buyer has a value for each item drawn independently according to (non-identical) …