Owing to their connection with generative adversarial networks (GANs), saddle-point
problems have recently attracted considerable interest in machine learning and beyond. By …

We examine global non-asymptotic convergence properties of policy gradient methods for
multi-agent reinforcement learning (RL) problems in Markov potential games (MPGs). To …

Potential games are arguably one of the most important and widely studied classes of
normal form games. They define the archetypal setting of multi-agent coordination as all …

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

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

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 …

We consider a multi-armed bandit game where N players compete for K arms for T turns.
Each player has different expected rewards for the arms, and the instantaneous rewards are …

This paper examines the long-run behavior of learning with bandit feedback in non-
cooperative concave games. The bandit framework accounts for extremely low-information …

Compared to minimization, the min-max optimization in machine learning applications is
considerably more convoluted because of the existence of cycles and similar phenomena …

Multiagent decision making over networks has recently attracted an exponentially growing
number of researchers from the systems and control community. The area has gained …