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Learning for Decision and Control in Stochastic Networks [electronic resource] / by Longbo Huang.

By: Huang, Longbo [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Learning, Networks, and Algorithms: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2023Edition: 1st ed. 2023.Description: XI, 71 p. 8 illus., 7 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031315978.Subject(s): Computer Networks | Stochastic processes | Machine learning | Application software | Computer science | Mathematical optimization | Computer Networks | Stochastic Networks | Machine Learning | Computer and Information Systems Applications | Computer Science | OptimizationAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621.3821 | 004.6 Online resources: Click here to access online
Contents:
Introduction -- The Stochastic Network Model -- Network Optimization Techniques -- Learning Network Decisions -- Summary and Discussions.
In: Springer Nature eBookSummary: 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.
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Introduction -- The Stochastic Network Model -- Network Optimization Techniques -- Learning Network Decisions -- Summary and Discussions.

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.

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