Abstract

Multi-Agent Reinforcement Learning (MARL), an area of machine learning in which a collective of agents learn to optimally interact in a shared environment, boasts a growing array of applications in modern life, from autonomous driving and multi-robot factories to automated trading and energy network management. This tutorial provides an introduction to the models, solution concepts, algorithmic ideas, technical challenges, and modern approaches in MARL . The tutorial first introduces the field’s foundations, including basics of reinforcement learning theory and algorithms, interactive game models, different solution concepts for games, and the algorithmic ideas underpinning MARL research. It then details contemporary MARL algorithms which leverage deep learning techniques, covering ideas such as centralized training with decentralized execution, value decomposition, parameter sharing, and self-play.The tutorial follows the new MIT Press textbook “Multi-Agent Reinforcement Learning: Foundations and Modern Approaches”, available at www.marl-book.com.