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The Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework uses a Kinematic-Aware Inference Engine to reconstruct neighbor trajectories, addressing coordination collapse in sparse-signaling UAV networks. This method improves resilient coverage under bandwidth constraints by l...
The Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework uses a Kinematic-Aware Inference Engine to reconstruct neighbor trajectories, addressing coordination collapse in sparse-signaling UAV networks. This method improves resilient coverage under bandwidth constraints by l...
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This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Aware Inference Engine that...
Maintaining coordination in UAV swarms with limited bandwidth is critical for reliable aerial network coverage. PL-MARL's predictive inference approach helps overcome sparse signaling and information delays, enhancing operational resilience.
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