Note: this is the pre-print, non-final version of the book. If you spot any issues in the book (typos, errors in definitions, inaccurate descriptions, unclear figures/images, etc.), please report the issue via e-mail to:  issues ~at~ marl-book ~dot~ com

Download pre-print PDF (draft date: 25 August 2023)

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Stefano V. Albrecht, Filippos Christianos, and Lukas Schäfer. Multi-Agent Reinforcement Learning: Foundations and Modern Approaches. MIT Press, 2023.

@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 = {2023},
  url = {}

Table of Contents

Summary of Notation
List of Figures

Part 1: Foundations of Multi-Agent Reinforcement Learning

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

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

New and updated lecture slides for the book will be released in Q2 2024.

In the meantime, we provide the previous lecture slides used in the Reinforcement Learning course at Edinburgh University, which cover some parts of the book:

Please contact the book authors to get access to the Latex source files (uses beamer).