Fr. 110.00

Multi-Agent Reinforcement Learning - Foundations and Modern Approaches

English · Hardback

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Description

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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. ...

Product details

Authors Stefano V Albrecht, Stefano V. Albrecht, Filippos Christianos, Schaf, Lukas Schäfer, Stefano V. Albrecht
Publisher The MIT Press
 
Languages English
Product format Hardback
Released 17.12.2024
 
EAN 9780262049375
ISBN 978-0-262-04937-5
No. of pages 396
Dimensions 159 mm x 235 mm x 30 mm
Subjects Education and learning > Teaching preparation > Vocational needs

machine learning, COMPUTERS / Computer Science, Information technology: general issues, COMPUTERS / Data Science / Machine Learning, COMPUTERS / Data Science / Neural Networks

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