Optimization-Driven Deep Reinforcement Learning for Wireless Networks

Optimization-Driven Deep Reinforcement Learning for Wireless Networks
English | 2026 | ISBN: 3032229960 | 219 Pages | PDF EPUB (True) | 32 MB
This book explores the integration and interplay of model-based optimization and model-free deep reinforcement learning (DRL). It addresses the growing complexity of future wireless networks. This book begins with a concise overview of foundational DRL algorithms and then delves into advanced frameworks, including optimization-driven DRL, hierarchical DRL, multi-agent DRL, Bayesian-enhanced DRL, and Lyapunov-guided DRL. Each framework is illustrated through case studies in emerging scenarios such as intelligent reflecting surface (IRS)-assisted wireless communications, UAV-assisted wireless networks, backscatter-assisted relay communications, and mobile edge computing.