mlsanityData to 14 Oct 2026

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

arXiv 2304.08493 ↗First posted 23 Dec 2022cs.MA cs.AI cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 2 of its 3 references that are in the map. First posted on arXiv 23 Dec 2022.

Builds on

2 of 3 references.

References · 2

  1. 2022Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Controlcited by 26
  2. 2020Auction-based Charging Scheduling with Deep Learning Framework for Multi-Drone Networkscited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networkscited by 3
  2. 2026Online Security Learning in Cooperative Multi-Agent Systems under Hidden Byzantine Attackscited by 0