Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

Regularized learning is a fundamental technique in online optimization, machine learning,
and many other fields of computer science. A natural question that arises in this context is …

Multi-agent systems can be used to address problems in a variety of domains, including
robotics, distributed control, telecommunications, and economics. The complexity of many …

This paper examines the convergence of no-regret learning in games with continuous action
sets. For concreteness, we focus on learning via “dual averaging”, a widely used class of no …

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

We study the question of obtaining last-iterate convergence rates for no-regret learning
algorithms in multi-player games. We show that the optimistic gradient (OG) algorithm with a …

In game-theoretic learning, several agents are simultaneously following their individual
interests, so the environment is non-stationary from each player's perspective. In this context …

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 …

We investigate a class of reinforcement learning dynamics where players adjust their
strategies based on their actions' cumulative payoffs over time—specifically, by playing …