Multi-Agent Reinforcement Learning: Foundations and Modern Approaches

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News

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1 May 2024: The book can now be pre-ordered (e.g. MIT Press, Amazon, Penguin Random House) with scheduled release on 17 December 2024.
3 April 2024: China Machine Press Co. will publish the Chinese translated edition of the book in December 2025.

Citation

Stefano V. Albrecht, Filippos Christianos, and Lukas Schäfer. Multi-Agent Reinforcement Learning: Foundations and Modern Approaches. MIT Press, 2024.

@book{ marl-book,
  author = {Stefano V. Albrecht and Filippos Christianos and Lukas Sch\"afer},
  title = {Multi-Agent Reinforcement Learning: Foundations and Modern Approaches},
  publisher = {MIT Press},
  year = {2024},
  url = {https://www.marl-book.com}
}

Summary of Notation
List of Figures
Preface

Chapter 1: Introduction

Part 1: Foundations of Multi-Agent Reinforcement Learning

Chapter 2: Reinforcement Learning
Chapter 3: Games: Models of Multi-Agent Interaction
Chapter 4: Solution Concepts for Games
Chapter 5: Multi-Agent Reinforcement Learning in Games: First Steps and Challenges
Chapter 6: Multi-Agent Reinforcement Learning: Foundational Algorithms

Part 2: Multi-Agent Deep Reinforcement Learning: Algorithms and Practice

Chapter 7: Deep Learning
Chapter 8: Deep Reinforcement Learning
Chapter 9: Multi-Agent Deep Reinforcement Learning
Chapter 10: Multi-Agent Deep Reinforcement Learning in Practice
Chapter 11: Multi-Agent Environments

Appendix A: Surveys on Multi-Agent Reinforcement Learning

Book Codebase

The book comes with a codebase written in the Python programming language, which contains implementations of several MARL algorithms presented in the book. The primary purpose of the codebase is to provide algorithm code that is self-contained and easy to read.

GitHub repository for the book codebase.

Lecture Slides

Lecture slides for the book will be released in Q3 2024.

Endorsements (solicited by MIT Press)

"The authors meticulously bring reinforcement learning together with game theory to provide a foundation for research and application of multi-agent reinforcement learning. This book is the perfect starting point for a grounding in the field."
—Andrew Barto, Professor Emeritus, University of Massachusetts Amherst

"Multi-agent reinforcement learning is well positioned to be the next white-hot area of artificial intelligence and this book provides the essential background, concepts, and insights for understanding this exciting and important area of research."
—Michael L. Littman, Professor of Computer Science, Brown University

"This book is the first complete reference for the growing area of multi-agent reinforcement learning. It provides both an essential resource for newcomers to the field and a valuable perspective for established researchers."
—Peter Stone, Professor of Computer Science, The University of Texas at Austin

"A landmark textbook to multiagent reinforcement learning, combining game-theoretic foundations with state-of-the-art deep learning. This essential textbook delivers fundamental insights for newcomers, experts and practitioners, featuring real-world applications and advanced algorithms."
—Karl Tuyls, Professor of Computer Science, University of Liverpool

"This will become the standard text of the emerging field of multiagent reinforcement learning. It builds from foundational ideas, incorporating recent breakthroughs in deep learning. This book will help accelerate theoretical and practical progress."
—Mykel J. Kochenderfer, Professor of Aeronautics and Astronautics, Stanford University