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Learning for Decision and Control in Stochastic Networks
Author
Title
Learning for Decision and Control in Stochastic Networks [electronic resource] / by Longbo Huang.
ISBN
9783031315978
Edition
1st ed. 2023.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2023.
Physical Description
1 online resource (XI, 71 p.) 8 illus., 7 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer Nature Synthesis Collection of Technology.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
July 07, 2023
Series
Synthesis Lectures on Learning, Networks, and Algorithms,
Contents
Introduction
The Stochastic Network Model
Network Optimization Techniques
Learning Network Decisions
Summary and Discussions.
Subjects
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