Reinforcement Learning Based Joint Optimization of Active RIS Reflection and Power Allocation for Cognitive Radar Networks

Cognitive radar networks have emerged as a key enabler for intelligent unmanned aerial vehicle (UAV) and vehicular sensing, providing adaptive perception, dynamic resource management, and enhanced situational awareness in autonomous and multi-agent environments. Active reconfigurable intelligent surface (ARIS)-assisted cognitive radar systems can provide additional degrees of freedom in signal manipulation and resource allocation, thereby improving multi-platform localization and tracking performance. However, achieving high performance in ARIS-assisted radar systems under limited resources remains a major challenge, as it requires the joint optimization of the ARIS reflection coefficients and transmit power allocation. To address this problem, we propose a reinforcement learning based optimization framework. Specifically, a deep deterministic policy gradient (DDPG) algorithm is employed to optimize the ARIS reflection coefficients, while a multi-agent deep deterministic policy gradient (MADDPG) algorithm is utilized to cooperatively allocate transmit power among multiple radars. Moreover, an immediate reward mechanism and prioritized experience replay scheme are designed to accelerate convergence and stabilize the training process. Simulation results demonstrate that the proposed framework not only enhances system performance but also achieves faster convergence and more efficient resource utilization compared with conventional schemes, then providing a practical and scalable solution for intelligent radar sensing systems.