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Multi-Agent Reinforcement Learning: Foundations and Modern Approaches by Stefano V. Albrecht at Abbey's Bookshop,
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
— —
Stefano V. Albrecht Filippos Christianos

Multi-Agent Reinforcement Learning

Foundations and Modern Approaches

Stefano V. Albrecht,  Filippos Christianos,  Lukas Schäfer

$140

Hardback

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English
MIT Press
21 January 2025
Computing: general
The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL's models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.

The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL's models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.

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 text provides a lucid and rigorous introduction to the models, solution concepts, algorithmic ideas, technical challenges, and modern approaches in MARL. The book 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 book comes with its own MARL codebase written in Python, containing implementations of MARL algorithms that are self-contained and easy to read. Technical content is explained in easy-to-understand language and illustrated with extensive examples, illuminating MARL for newcomers while offering high-level insights for more advanced readers.

First textbook to introduce the foundations and applications of MARL, written by experts in the field Integrates reinforcement learning, deep learning, and game theory Practical focus covers considerations for running experiments and describes environments for testing MARL algorithms Explains complex concepts in clear and simple language Classroom-tested, accessible approach suitable for graduate students and professionals across computer science, artificial intelligence, and robotics

Resources include code and slides
By:   , ,
Imprint:   MIT Press
Country of Publication:   United States
Dimensions:   Height: 178mm,  Width: 114mm, 
Weight:   567g
ISBN:   9780262049375
ISBN 10:   0262049376
Pages:   394
Publication Date:  
Audience:   General/trade ,  ELT Advanced
Format:   Hardback
Publisher's Status:   Active

Stefano V. Albrecht is Associate Professor in the School of Informatics at the University of Edinburgh, where he leads the Autonomous Agents Research Group. His research focuses on the development of machine learning algorithms for autonomous systems control and decision making, with a particular focus on deep reinforcement learning and multi-agent interaction. Filippos Christianos is a research scientist in multi-agent deep reinforcement learning focusing on how MARL algorithms can be used efficiently and the author of multiple popular MARL-focused code libraries. Lukas Sch fer is a researcher focusing on the development of more generalizable, robust, and sample-efficient decision making using deep reinforcement learning, with a particular focus on multi-agent reinforcement learning.

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