Applications of RL for Discrete Problems in RIS-Assisted Communication Systems

This chapter focuses on the practical implementation of RIS-assisted communication systems, specifically addressing discrete optimization problems. It explores the application of DRL techniques, including Q-learning and DQL, to optimize RIS-assisted systems efficiently. It details how these algorithms can be adapted and formulated to tackle discrete optimization problems in different scenarios, taking into account various assumptions and system conditions. Through detailed analysis and discussion, the chapter showcases the potential of DRL techniques in optimizing discrete settings and offers valuable insights for researchers interested in realizing the potential of RIS-assisted communication systems through intelligent and practical decision-making.