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

2022/12/23 by Chanyoung Park, Park, Chanyoung, Haemin Lee +7 · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multiagent Systems (cs.MA) #Robotics (cs.RO) #UAV Applications and Optimization #Video Surveillance and Tracking Methods

paper · pdf · doi:10.48550/arxiv.2304.08493

openalex publication_date 2022/12/23 · openalex created_date 2023/04/22 · openalex updated_date 2026/07/28

Abstract

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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