Reconfigurable intelligent surface (RIS) has emerged as a cutting-edge technology for beyond 5G and 6G networks due to its low-cost hardware production, nearly passive nature, easy deployment, communication without new waves, and energy-saving benefits. Unmanned aerial vehicle (UAV)-assisted wireless networks significantly enhance network coverage.Resource allocation and real-time decision-making optimisation play a pivotal role in approaching the optimal performance in UAV- and RIS-aided wireless communications. But the existing contributions typically assume having a static environment and often ignore the stringent flight time constraints in real-life applications. It is crucial to improve the decision-making time for meeting the stringent requirements of UAV-assisted wireless networks. Deep reinforcement learning (DRL), which is a combination of reinforcement learning and neural networks, is used to maximise network performance, reduce power consumption, and improve the processing time for real-time applications. DRL algorithms can help UAVs and RIS work fully autonomously, reduce energy consumption and operate optimally in an unexpected environment.This co-authored book explores the many challenges arising from real-time and autonomous decision-making for 6G. The goal is to provide readers with comprehensive insights into the models and techniques of deep reinforcement learning and its applications in 6G networks and internet-of-things with the support of UAVs and RIS.Deep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications is aimed at a wide audience of researchers, practitioners, scientists, professors and advanced students in engineering, computer science, information technology, and communication engineering, and networking and ubiquitous computing professionals.

Reconfigurable intelligent surface (RIS) has emerged as a cutting-edge technology for beyond 5G and 6G networks due to its low-cost hardware production, nearly passive nature, easy deployment, communication without new waves, and energy-saving benefits. Unmanned aerial vehicle (UAV)-assisted wireless networks significantly enhance network coverage.Resource allocation and real-time decision-making optimisation play a pivotal role in approaching the optimal performance in UAV- and RIS-aided wireless communications. But the existing contributions typically assume having a static environment and often ignore the stringent flight time constraints in real-life applications. It is crucial to improve the decision-making time for meeting the stringent requirements of UAV-assisted wireless networks. Deep reinforcement learning (DRL), which is a combination of reinforcement learning and neural networks, is used to maximise network performance, reduce power consumption, and improve the processing time for real-time applications. DRL algorithms can help UAVs and RIS work fully autonomously, reduce energy consumption and operate optimally in an unexpected environment.This co-authored book explores the many challenges arising from real-time and autonomous decision-making for 6G. The goal is to provide readers with comprehensive insights into the models and techniques of deep reinforcement learning and its applications in 6G networks and internet-of-things with the support of UAVs and RIS.Deep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications is aimed at a wide audience of researchers, practitioners, scientists, professors and advanced students in engineering, computer science, information technology, and communication engineering, and networking and ubiquitous computing professionals.