mlsanityData to 7 Oct 2026

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

arXiv 2302.04445 ↗First posted 9 Feb 2023cs.MA cs.AI cs.LGcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 24 of its 95 references that are in the map. First posted on arXiv 9 Feb 2023.

Builds on

24 of 95 references.

References · 24

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  3. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 475
  4. 2016Learning Multiagent Communication with Backpropagationcited by 355
  5. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
  6. 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 157
  7. 2018Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attackcited by 123
  8. 2019Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneckcited by 101
  9. 2021Parametrized quantum policies for reinforcement learningcited by 87
  10. 2020Reinforcement Learning with Quantum Variational Circuitscited by 74
  11. 2020Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applicationscited by 62
  12. 2016Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challengescited by 40
12 more
  1. 2022Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Controlcited by 25
  2. 2022Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Designcited by 25
  3. 2022Quantum Multi-Agent Meta Reinforcement Learningcited by 23
  4. 2020Artificial Intelligence for UAV-enabled Wireless Networks: A Surveycited by 11
  5. 2023Quantum Multi-Agent Actor-Critic Neural Networks for Internet-Connected Multi-Robot Coordination in Smart Factory Managementcited by 8
  6. 2021Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networkscited by 7
  7. 2018On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networkscited by 6
  8. 2022Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Controlcited by 5
  9. 2019On-Demand Density-Aware UAV Base Station 3D Placement for Arbitrarily Distributed Users with Guaranteed Data Ratescited by 4
  10. 2016The New Frontier in RAN Heterogeneity: Multi-tier Drone-Cellscited by 3
  11. 2021Energy-Efficient 3D Deployment of Aerial Access Points in a UAV Communication Systemcited by 2
  12. 2022Incentivizing Proof-of-Stake Blockchain for Secured Data Collection in UAV-Assisted IoT: A Multi-Agent Reinforcement Learning Approachcited by 2

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 2.

5 papers.

  1. 2024An Introduction to Quantum Reinforcement Learning (QRL)cited by 7
  2. 2023Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperationcited by 6
  3. 2023Realizing Stabilized Landing for Computation-Limited Reusable Rockets: A Quantum Reinforcement Learning Approachcited by 1
  4. 2024Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networkscited by 1
  5. 2023Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipescited by 0