2025 Asian Conference on Communication and Networks (ASIANComNet 2025)

Reconfigurable intelligent surfaces (RIS) enable control of radio propagation via large arrays of passive reflecting elements. Optimizing RIS phase profiles for spectral efficiency is challenging due to high-dimensional continuous actions and non-convex channel coupling. We cast RIS beamforming as a sequential decision problem and evaluate four reinforcement-learning (RL) agents—A2C, Graph-Neural-Network Proximal Policy Optimization (GNN-PPO), Soft Actor–Critic (SAC), and Quantile-Regression PPO (QR-PPO)—in a realistic simulator with mobility, dual-slope log-distance path loss, shadowing, and Rician fading. Using a common protocol and PCA/GNN feature extraction, we compare agents on \textbf{rate} (mean and variability), \textbf{tail risk} via CVaR at 5\%, mean SNR, and wall-clock cost. \textbf{GNN-PPO} attains the best mean rate, the \emph{lowest} variability, the \emph{highest} CVaR at 5\% (strong tail performance), and the highest mean SNR. \textbf{A2C} is the compute-efficiency winner with the shortest total time, \textbf{SAC} provides a balanced compromise, while \textbf{QR-PPO} is cost-inefficient and underperforms in the tails under our configuration. We discuss design insights and directions for scalable, risk-aware RIS control.

Reconfigurable intelligent surfaces,Reinforcement Learning,deep learning,wireless communications,6G,beamforming,GNN-PPO,SAC,CVaR