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

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arxiv 2304.08493 v1 pith:P3TA45NF submitted 2022-12-23 cs.MA cs.AIcs.LGcs.RO

Chanyoung Park , Haemin Lee , Won Joon Yun , Soyi Jung , Joongheon Kim This is my paper · ORCID
classification cs.MAcs.AIcs.LGcs.RO
keywords accessapplicationsmobileaerialautonomouscentralizedlearningmulti-agent
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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.

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