PSO-Aided Reinforcement Learning for Beam and Power Management in RIS-Assisted mmWave Networks

This paper proposes a novel Particle Swarm Optimization-Aided Reinforcement Learning (PSO-RL) framework for efficient beam and power management in reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) networks. To optimize dynamic beam coordination and resource allocation, we introduce a reinforcement learning (RL)-based approach that integrates value and policy networks, with the policy network enhanced by a target policy network (TPN). By employing PSO, we refine TPN parameters to accelerate convergence and enhance solution exploration. Simulation results validate the effectiveness of the proposed PSO-RL framework, demonstrating significant performance improvements in RISassisted network deployments. The method enhances system capacity by 29.75 % and improves edge user capacity by 36.65 %. These results highlight the potential of PSO-RL for optimizing beam allocation in dynamic urban network environments.