Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications

2022-12-23 · Chanyoung Park, Haemin Lee, Won Joon Yun, Soyi Jung, Joongheon Kim

This paper proposes a novel centralized training and distributed execution (CTDE)-based multi-agent deep reinforcement learning (MADRL) method for multiple unmanned aerial vehicles (UAVs) control in autonomous mobile access applications. For the purpose, a single neural network is utilized in centralized training for cooperation among multiple agents while maximizing the total quality of service (QoS) in mobile access applications.

📄 PDF Abstract BibTeX arXiv:2304.08493

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Deep Reinforcement LearningMulti-agent Reinforcement Learning reinforcement-learning Reinforcement Learning

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