mlsanityData to 7 Oct 2026

Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control

arXiv 2201.05843 ↗First posted 15 Jan 2022eess.SY cs.AI cs.LGcited by 25

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Abstract (arXiv)

CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a key technology for security in smart city environments. This paper creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services. UAVs should be deployed to cover a large area while minimize overlapping and shadow areas for a reliable surveillance system. However, the operation of UAVs is subject to high uncertainty, necessitating autonomous recovery systems. This work develops a multi-agent deep reinforcement learning-based management scheme for reliable industry surveillance in smart city applications. The core idea this paper employs is autonomously replenishing the UAV's deficient network requirements with communications. Via intensive simulations, our proposed algorithm outperforms the state-of-the-art algorithms in terms of surveillance coverage, user support capability, and computational costs.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 14 of its 42 references that are in the map. First posted on arXiv 15 Jan 2022.

Builds on

14 of 42 references.

References · 14

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  3. 2015Trust Region Policy Optimizationcited by 2,503
  4. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  5. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 561
  6. 2016Learning Multiagent Communication with Backpropagationcited by 355
  7. 2018Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applicationscited by 122
  8. 2020Auction-based Charging Scheduling with Deep Learning Framework for Multi-Drone Networkscited by 12
  9. 2020Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networkscited by 8
  10. 2019Deep Learning for Optimal Deployment of UAVs with Visible Light Communicationscited by 7
  11. 2018On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networkscited by 6
  12. 2021Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehiclescited by 4
2 more
  1. 2018UAV Communications Based on Non-Orthogonal Multiple Accesscited by 3
  2. 2020Machine Learning for Predictive Deployment of UAVs with Multiple Accesscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 8; 2024: 5; 2025: 5; 2026: 2.

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  1. 2023Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influencecited by 16
  2. 2023Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackerscited by 15
  3. 2023Vision-based Learning for Drones: A Surveycited by 9
  4. 2025Learning Generalizable Skills from Offline Multi-Task Data for Multi-Agent Cooperationcited by 8
  5. 2023Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperationcited by 6
  6. 2025Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learningcited by 6
  7. 2022Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Controlcited by 5
  8. 2023Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systemscited by 5
  9. 2023Robust Multi-agent Communication via Multi-view Message Certificationcited by 3
  10. 2024Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learningcited by 3
  11. 2022Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applicationscited by 2
  12. 2023Deep Reinforcement Learning Based Placement for Integrated Access Backhauling in UAV-Assisted Wireless Networkscited by 2
13 more
  1. 2024Offline Fictitious Self-Play for Competitive Gamescited by 1
  2. 2024CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systemscited by 1
  3. 2026Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learningcited by 1
  4. 2022Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networkscited by 0
  5. 2022Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learningcited by 0
  6. 2022Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobilitycited by 0
  7. 2023Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipescited by 0
  8. 2024A hierarchical control framework for autonomous decision-making systems: Integrating HMDP and MPCcited by 0
  9. 2024Navigating the Smog: A Cooperative Multi-Agent RL for Accurate Air Pollution Mapping through Data Assimilationcited by 0
  10. 2025Sustainable Smart Farm Networks: Enhancing Resilience and Efficiency with Decision Theory-Guided Deep Reinforcement Learningcited by 0
  11. 2025Integrated Communication and Control for Energy-Efficient UAV Swarms: A Multi-Agent Reinforcement Learning Approachcited by 0
  12. 2025Distributed Area Coverage with High Altitude Balloons Using Multi-Agent Reinforcement Learningcited by 0
  13. 2026Online Security Learning in Cooperative Multi-Agent Systems under Hidden Byzantine Attackscited by 0