mlsanityData to 7 Oct 2026

Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning

arXiv 2111.14598 ↗First posted 29 Nov 2021cs.LG cs.MA cs.ROcited by 6

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Abstract (arXiv)

Safety is the primary concern when it comes to air traffic. In-flight safety between Unmanned Aircraft Vehicles (UAVs) is ensured through pairwise separation minima, utilizing conflict detection and resolution methods. Existing methods mainly deal with pairwise conflicts, however due to an expected increase in traffic density, encounters with more than two UAVs are likely to happen. In this paper, we model multi-UAV conflict resolution as a multi-agent reinforcement learning problem. We implement an algorithm based on graph neural networks where cooperative agents can communicate to jointly generate resolution maneuvers. The model is evaluated in scenarios with 3 and 4 present agents. Results show that agents are able to successfully solve the multi-UAV conflicts through a cooperative strategy.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 12 of its 48 references that are in the map. First posted on arXiv 29 Nov 2021.

Builds on

12 of 48 references.

References · 12

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  6. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  7. 2018An Introduction to Deep Reinforcement Learningcited by 201
  8. 2018A Survey and Critique of Multiagent Deep Reinforcement Learningcited by 173
  9. 2018Graph Convolutional Reinforcement Learningcited by 148
  10. 2018Relational Deep Reinforcement Learningcited by 72
  11. 2018Attention Models in Graphs: A Surveycited by 41
  12. 2020A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurancecited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 0; 2025: 0; 2026: 2.

6 papers.

  1. 2022A Survey on Reinforcement Learning in Aviation Applicationscited by 19
  2. 2022Data-driven prediction of Air Traffic Controllers reactions to resolving conflictscited by 0
  3. 2022From Single Aircraft to Communities: A Neutral Interpretation of Air Traffic Complexity Dynamicscited by 0
  4. 2023Two-step dynamic obstacle avoidancecited by 0
  5. 2026Separation Assurance between Heterogeneous Fleets of Small Unmanned Aerial Systems via Multi-Agent Reinforcement Learningcited by 0
  6. 2026Conflict Resolution under Degraded Surveillance in Air Corridors Using Multi-Agent Reinforcement Learningcited by 0