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

arXiv: https://arxiv.org/abs/2201.05843 · HTML: https://mlsanity.com/paper/2201.05843/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 15 Jan 2022.

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018)
- [Auction-based Charging Scheduling with Deep Learning Framework for Multi-Drone Networks](https://mlsanity.com/paper/2001.03611/) (2020)
- [Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks](https://mlsanity.com/paper/2005.12394/) (2020)
- [Deep Learning for Optimal Deployment of UAVs with Visible Light Communications](https://mlsanity.com/paper/1912.00752/) (2019)
- [On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks](https://mlsanity.com/paper/1804.08415/) (2018)
- [UAV Communications Based on Non-Orthogonal Multiple Access](https://mlsanity.com/paper/1809.05767/) (2018)
- [Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2106.14203/) (2021)
- [Machine Learning for Predictive Deployment of UAVs with Multiple Access](https://mlsanity.com/paper/2003.02631/) (2020)

## Built on by (most cited first, 26 of 26)

- [Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence](https://mlsanity.com/paper/2302.03322/) (2023)
- [Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers](https://mlsanity.com/paper/2305.05909/) (2023)
- [Vision-based Learning for Drones: A Survey](https://mlsanity.com/paper/2312.05019/) (2023)
- [Learning Generalizable Skills from Offline Multi-Task Data for Multi-Agent Cooperation](https://mlsanity.com/paper/2503.21200/) (2025)
- [Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation](https://mlsanity.com/paper/2308.01519/) (2023)
- [Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2502.02844/) (2025)
- [Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control](https://mlsanity.com/paper/2210.00945/) (2022)
- [Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems](https://mlsanity.com/paper/2302.04445/) (2023)
- [Robust Multi-agent Communication via Multi-view Message Certification](https://mlsanity.com/paper/2305.13936/) (2023)
- [Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2408.13567/) (2024)
- [Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications](https://mlsanity.com/paper/2304.08493/) (2022)
- [Deep Reinforcement Learning Based Placement for Integrated Access Backhauling in UAV-Assisted Wireless Networks](https://mlsanity.com/paper/2312.14247/) (2023)
- [Offline Fictitious Self-Play for Competitive Games](https://mlsanity.com/paper/2403.00841/) (2024)
- [CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems](https://mlsanity.com/paper/2406.17425/) (2024)
- [NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation](https://mlsanity.com/paper/2605.15559/) (2026)
- [Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.18024/) (2026)
- [Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networks](https://mlsanity.com/paper/2209.00862/) (2022)
- [Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2211.05952/) (2022)
- [Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility](https://mlsanity.com/paper/2211.06890/) (2022)
- [Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipes](https://mlsanity.com/paper/2307.09711/) (2023)
- [A hierarchical control framework for autonomous decision-making systems: Integrating HMDP and MPC](https://mlsanity.com/paper/2401.06833/) (2024)
- [Navigating the Smog: A Cooperative Multi-Agent RL for Accurate Air Pollution Mapping through Data Assimilation](https://mlsanity.com/paper/2407.12539/) (2024)
- [Sustainable Smart Farm Networks: Enhancing Resilience and Efficiency with Decision Theory-Guided Deep Reinforcement Learning](https://mlsanity.com/paper/2505.03721/) (2025)
- [Integrated Communication and Control for Energy-Efficient UAV Swarms: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2509.23905/) (2025)
- [Distributed Area Coverage with High Altitude Balloons Using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2510.03823/) (2025)
- [Online Security Learning in Cooperative Multi-Agent Systems under Hidden Byzantine Attacks](https://mlsanity.com/paper/2608.06520/) (2026)

## 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.