# Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems

arXiv: https://arxiv.org/abs/2302.04445 · HTML: https://mlsanity.com/paper/2302.04445/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 95 references that are in the map.
- First posted on arXiv 9 Feb 2023.

## Builds on (most cited first, 24 of 24)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [Variational Quantum Circuits for Deep Reinforcement Learning](https://mlsanity.com/paper/1907.00397/) (2019)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack](https://mlsanity.com/paper/1811.09310/) (2018)
- [Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck](https://mlsanity.com/paper/1910.12911/) (2019)
- [Parametrized quantum policies for reinforcement learning](https://mlsanity.com/paper/2103.05577/) (2021)
- [Reinforcement Learning with Quantum Variational Circuits](https://mlsanity.com/paper/2008.07524/) (2020)
- [Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications](https://mlsanity.com/paper/2008.02608/) (2020)
- [Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges](https://mlsanity.com/paper/1602.03602/) (2016)
- [Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control](https://mlsanity.com/paper/2201.05843/) (2022)
- [Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design](https://mlsanity.com/paper/2203.10443/) (2022)
- [Quantum Multi-Agent Meta Reinforcement Learning](https://mlsanity.com/paper/2208.11510/) (2022)
- [Artificial Intelligence for UAV-enabled Wireless Networks: A Survey](https://mlsanity.com/paper/2009.11522/) (2020)
- [Quantum Multi-Agent Actor-Critic Neural Networks for Internet-Connected Multi-Robot Coordination in Smart Factory Management](https://mlsanity.com/paper/2301.04012/) (2023)
- [Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks](https://mlsanity.com/paper/2106.09274/) (2021)
- [On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks](https://mlsanity.com/paper/1804.08415/) (2018)
- [Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control](https://mlsanity.com/paper/2210.00945/) (2022)
- [On-Demand Density-Aware UAV Base Station 3D Placement for Arbitrarily Distributed Users with Guaranteed Data Rates](https://mlsanity.com/paper/1904.10881/) (2019)
- [The New Frontier in RAN Heterogeneity: Multi-tier Drone-Cells](https://mlsanity.com/paper/1604.00381/) (2016)
- [Energy-Efficient 3D Deployment of Aerial Access Points in a UAV Communication System](https://mlsanity.com/paper/2104.06314/) (2021)
- [Incentivizing Proof-of-Stake Blockchain for Secured Data Collection in UAV-Assisted IoT: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2207.02705/) (2022)

## Built on by (most cited first, 5 of 5)

- [An Introduction to Quantum Reinforcement Learning (QRL)](https://mlsanity.com/paper/2409.05846/) (2024)
- [Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation](https://mlsanity.com/paper/2308.01519/) (2023)
- [Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2406.16994/) (2024)
- [Realizing Stabilized Landing for Computation-Limited Reusable Rockets: A Quantum Reinforcement Learning Approach](https://mlsanity.com/paper/2310.06541/) (2023)
- [Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipes](https://mlsanity.com/paper/2307.09711/) (2023)

## Abstract (arXiv)

This paper proposes a novel algorithm, named quantum multi-agent actor-critic networks (QMACN) for autonomously constructing a robust mobile access system employing multiple unmanned aerial vehicles (UAVs). In the context of facilitating collaboration among multiple unmanned aerial vehicles (UAVs), the application of multi-agent reinforcement learning (MARL) techniques is regarded as a promising approach. These methods enable UAVs to learn collectively, optimizing their actions within a shared environment, ultimately leading to more efficient cooperative behavior. Furthermore, the principles of a quantum computing (QC) are employed in our study to enhance the training process and inference capabilities of the UAVs involved. By leveraging the unique computational advantages of quantum computing, our approach aims to boost the overall effectiveness of the UAV system. However, employing a QC introduces scalability challenges due to the near intermediate-scale quantum (NISQ) limitation associated with qubit usage. The proposed algorithm addresses this issue by implementing a quantum centralized critic, effectively mitigating the constraints imposed by NISQ limitations. Additionally, the advantages of the QMACN with performance improvements in terms of training speed and wireless service quality are verified via various data-intensive evaluations. Furthermore, this paper validates that a noise injection scheme can be used for handling environmental uncertainties in order to realize robust mobile access.