Multi-Agent Reinforcement Learning

Name: Multi-Agent Reinforcement Learning
Brand: The MIT Press
SKU: 9780262049375-PRUSDTR
Price: 459.0 HKD
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Foundations and Modern Approaches

Stefano V. Albrecht (Author) • Filippos Christianos (Author) • Lukas Schäfer (Author) ... more

Edition: US - Hardback

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English

396 pages • 6.0 x 9.0 in

Approx. weight: 1.25 lb

Publication date: 17 Dec,2024

Barcode: 9780262049375 • The MIT Press

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By: Stefano V. Albrecht, Filippos Christianos, Lukas Schäfer

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