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arXiv AI By Faisal Al-Kamali, Hussein A. Ammar, Francois Chan, James H. Bayes, Yasser Gadallah, Mohamed H. Ahmed

Threat-Aware Energy-Efficient Deployment for Dynamic UAV Networks: A Multi-Agent RL Approach

Read the original on arXiv AI →

The paper introduces a three‑step framework for deploying multi‑UAV networks in threat‑prone environments. First, a threat‑aware K‑means algorithm determines the minimal number of UAVs and safe initial positions. Second, an optimal matching stage assigns UAVs to these positions to reduce energy use. Third, a threat‑aware multi‑agent twin delayed deep deterministic policy gradient (MATD3) algorithm dynamically optimizes UAV trajectories, power, and user associations, achieving zero safety violations and superior energy efficiency compared to other learning methods and heuristic baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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