Learning for Decision and Control in Stochastic Networks

Introduction to the Learning-Augmented Network Optimization (LANO) Paradigm

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.

Foundational Theoretical Tools

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.

Learning-Based Methods and Applications

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.

Future Directions and Challenges

The authors also provide a discussion on potential future directions and challenges.