AI RESEARCH
Learn for Variation: Variationally Guided AAV Trajectory Learning in Differentiable Environments
arXiv CS.LG
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ArXi:2603.18853v1 Announce Type: cross Autonomous aerial vehicles (AAVs) empower sixth-generation (6G) Internet-of-Things (IoT) networks through mobility-driven data collection. However, conventional reward-driven reinforcement learning for AAV trajectory planning suffers from severe credit assignment issues and