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Learning for Decision and Control in Stochastic Networks (Synthesis Lectures on Learning, Networks, and Algorithms)
Learning for Decision and Control in Stochastic Networks (Synthesis Lectures on Learning, Networks, and Algorithms)
Longbo Huang
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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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