Huang, Longbo.
Learning for Decision and Control in Stochastic Networks [electronic resource] / by Longbo Huang. - 1st ed. 2023. - XI, 71 p. 8 illus., 7 illus. in color. online resource. - Synthesis Lectures on Learning, Networks, and Algorithms, 2690-4314 . - Synthesis Lectures on Learning, Networks, and Algorithms, .
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.
9783031315978
10.1007/978-3-031-31597-8 doi
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.
Optimization.
TK5105.5-.9
621.3821 004.6
Learning for Decision and Control in Stochastic Networks [electronic resource] / by Longbo Huang. - 1st ed. 2023. - XI, 71 p. 8 illus., 7 illus. in color. online resource. - Synthesis Lectures on Learning, Networks, and Algorithms, 2690-4314 . - Synthesis Lectures on Learning, Networks, and Algorithms, .
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.
9783031315978
10.1007/978-3-031-31597-8 doi
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.
Optimization.
TK5105.5-.9
621.3821 004.6