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

arXiv: https://arxiv.org/abs/2304.08493 · HTML: https://mlsanity.com/paper/2304.08493/

## 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 (most cited first, 2 of 2)

- [Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control](https://mlsanity.com/paper/2201.05843/) (2022)
- [Auction-based Charging Scheduling with Deep Learning Framework for Multi-Drone Networks](https://mlsanity.com/paper/2001.03611/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [Online Security Learning in Cooperative Multi-Agent Systems under Hidden Byzantine Attacks](https://mlsanity.com/paper/2608.06520/) (2026)

## 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.