Challenges and Future Work

This chapter addresses the challenges and explores future directions in the field of DRL in RIS-assisted wireless systems. The chapter begins by discussing the challenges associated with DRL, particularly focusing on hyperparameter tuning and problem design. The chapter further provides insights into addressing these challenges effectively and presents three promising directions. First, it introduces the concept of hybrid RL, which combines RL with supervised learning approaches. This integration aims to leverage the strengths of both methods and enhances the overall learning performance. The chapter then explores the potential of multi-agent RL, emphasizing the benefits of collaborative decision-making and coordination among multiple RL agents. Additionally, it discusses the potential of incorporating transfer learning into DRL applications. Concluding remarks summarize the key findings and contributions of the book, facilitating a solid background on deploying DRL to optimize RIS-assisted wireless communication systems.