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This paper explores a near-field integrated sensing and communication (ISAC) network empowered by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). This architecture offers seamless 360° coverage, supporting the coexistence of aerial and ground users in the low-altitude economy, while leveraging near-field propagation to enhance both communication and sensing performance. However, the heterogeneity of users and the coupling between sensing, communication, and energy consumption pose challenges for efficient beamforming. To this end, we propose a joint beamforming and phase shift optimization framework to minimize average power consumption under communication delay and sensing constraints. To cope with the long-term characteristics, we design a Lyapunov-based deep deterministic policy gradient (LyDDPG) algorithm to transform long-term objectives into slot-level decisions, achieving stable performance in dynamic environments. To further improve constraint satisfaction and policy convergence, we further propose a robust policy deep reinforcement learning (RPDRL) algorithm, which integrates policy-based reinforcement learning with successive convex approximation. The Lyapunov transformation is embedded into the RPDRL to dynamically adjust weighting factors, accelerating policy learning. Simulation results demonstrate the effectiveness and superiority of the proposed methods in achieving energy-efficient, constraint-aware beamforming in near-field ISAC networks.
Cai et al. (Sat,) studied this question.