Foundations of Multi-Agent Learning in Dynamic Environments: Where Reinforcement Learning Meets Strategic Decision-Making

Abstract

Recent years have witnessed tremendous successes of learning for sequential decision-making, and in particular, Reinforcement Learning (RL). Prominent application examples include playing Go and video games, robotics, autonomous driving, and recently large language models. Most such success stories naturally involve "multi-agents". Hence, there has been surging research interest in advancing Multi-Agent Learning in Dynamic Environments, particularly, multi-agent RL (MARL), to which my research has led and made significant contributions. My work has established both sample and computational complexities of learning in Stochastic Games, the most fundamental model of MARL, and advocated a unique Economics perspective of independent learning in Stochastic Games. My work has also initiated the recent studies of distributed and networked MARL, with applications in robust adversarial RL, offline RL, and Robotics. This paper will survey my notable contributions along this journey of developing the foundations of multi-agent learning in dynamic environments.