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.