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

arXiv:2609.30690 · cs.IT, cs.AI, cs.LG, eess.SP, math.IT · Submitted 2026-09-25 · Read on arXiv

cs.IT, cs.AI, cs.LG, eess.SP, math.IT

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Clustering — 73× in this paper · explained in 2 episodes
• Actor — 25× in this paper · explained in 3 episodes
• Reinforcement Learning — 15× in this paper · explained in 4 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 7× in this paper · explained in 2 episodes
• Energy Efficiency — 5× in this paper · explained in 3 episodes
• Reward Function — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Action Space — 2× in this paper · explained in 1 episode
• Objective Function — 2× in this paper · explained in 2 episodes
• Q-learning — 2× in this paper · explained in 3 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Actor-Critic Framework — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Communication Overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Mobile Edge Computing — 1× in this paper · explained in 2 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes

Sources

• Continuous control with deep reinforcement learning
• Addressing Function Approximation Error in Actor-Critic Methods
• Prioritized Experience Replay
• Adam: A Method for Stochastic Optimization

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