Multi-agent Control of UAVs Using Deep Reinforcement Learning

This paper presents a novel control framework for managing a group of unmanned aerial vehicles using multi-agent deep reinforcement learning. The approach leverages actor–critic architectures, centralized training with decentralized execution, and shared experience replay to enable autonomous coordination in dynamic environments. Simulation results confirm improved tracking accuracy, reduced collision rates, and increased coverage efficiency. The study also compares the proposed system against baseline methods and outlines future work for real-world adaptation. The novelty lies in applying multi-agent deep reinforcement learning to a continuous unmanned aerial vehicle control task in cluttered environments with limited sensing.