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.

• Chapter 1: Artificial intelligence, machine learning and deep learning
• Chapter 2: Deep neural networks
• Chapter 3: Markov decision process
• Chapter 4: Value function approximation for continuous state-action space
• Chapter 5: Policy search methods for reinforcement learning
• Chapter 6: Actor-Critic learning
• Chapter 7: UAV-assisted 6G communications
• Chapter 8: Distributed deep deterministic policy gradient for power allocation control in UAV-to-UAV-based communications
• Chapter 9: Non-cooperative energy efficient power allocation game in UAV-to-UAV communication: A multi-agent deep reinforcement learning approach
• Chapter 10: Real-time energy harvesting aided scheduling in UAV-assisted D2D networks
• Chapter 11: 3D trajectory design and data collection in UAV-assisted networks
• Chapter 12: RIS-assisted 6G communications
• Chapter 13: Real-time optimisation in RIS-assisted D2D communications
• Chapter 14: RIS-assisted UAV communications for IoT with wireless power transfer using deep reinforcement learning
• Chapter 15: Multi-agent learning in networks supported by RIS and multi-UAVs