Learning for Decision and Control in Stochastic Networks
Published on 21. June 2024
Book
Paperback/Softback
XI, 71 pages
978-3-031-31599-2 (ISBN)
Description
This book introduces the Learning-Augmented Network Optimization (LANO) paradigm, which interconnects network optimization with the emerging AI theory and algorithms and has been receiving a growing attention in network research. The authors present the topic based on a general stochastic network optimization model, and review several important theoretical tools that are widely adopted in network research, including convex optimization, the drift method, and mean-field analysis. The book then covers several popular learning-based methods, i.e., learning-augmented drift, multi-armed bandit and reinforcement learning, along with applications in networks where the techniques have been successfully applied. The authors also provide a discussion on potential future directions and challenges.
Reviews / Votes
"This monograph gives an overview of a class of algorithms for optimization of queuing networks in wireless and related networks. . This is followed by approaches based on multi-armed bandits and the approaches that use standard reinforcement learning algorithms grounded in the underlying Markov decision theoretic framework. It concludes with some pointers for future work." (Vivek S. Borkar, Mathematical Reviews, August, 2024)
More details
Series
Edition
2023 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
7 farbige Abbildungen, 1 s/w Abbildung
XI, 71 p. 8 illus., 7 illus. in color.
Dimensions
Height: 240 mm
Width: 168 mm
Thickness: 6 mm
Weight
158 gr
ISBN-13
978-3-031-31599-2 (9783031315992)
DOI
10.1007/978-3-031-31597-8
Schweitzer Classification
Other editions
Additional editions
Book
06/2023
Springer
€58.84
Shipment within 15-20 days
Person
Longbo Huang, Ph.D. is an Associate Professor at the Institute for Interdisciplinary Information Sciences (IIIS) at Tsinghua University, Beijing, China. He received his Ph.D. in EE from the University of Southern California, and then worked as a postdoctoral researcher in the EECS dept. at University of California at Berkeley before joining IIIS. Dr. Huang previously held visiting positions at the LIDS lab at MIT, the Chinese University of Hong Kong, Bell-labs France, and Microsoft Research Asia (MSRA). He was also a visiting scientist at the Simons Institute for the Theory of Computing at UC Berkeley in Fall 2016. Dr. Huang's research focuses on decision intelligence (AI for decisions), including deep reinforcement learning, online learning and reinforcement learning, learning-augmented network optimization, distributed optimization and machine learning.
Content
Introduction.- The Stochastic Network Model.- Network Optimization Techniques.- Learning Network Decisions.- Summary and Discussions.