# Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning

arXiv: https://arxiv.org/abs/2111.14598 · HTML: https://mlsanity.com/paper/2111.14598/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 12 of 12)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [A Survey and Critique of Multiagent Deep Reinforcement Learning](https://mlsanity.com/paper/1810.05587/) (2018)
- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [Relational Deep Reinforcement Learning](https://mlsanity.com/paper/1806.01830/) (2018)
- [Attention Models in Graphs: A Survey](https://mlsanity.com/paper/1807.07984/) (2018)
- [A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance](https://mlsanity.com/paper/2003.08353/) (2020)

## Built on by (most cited first, 6 of 6)

- [A Survey on Reinforcement Learning in Aviation Applications](https://mlsanity.com/paper/2211.02147/) (2022)
- [Data-driven prediction of Air Traffic Controllers reactions to resolving conflicts](https://mlsanity.com/paper/2205.09539/) (2022)
- [From Single Aircraft to Communities: A Neutral Interpretation of Air Traffic Complexity Dynamics](https://mlsanity.com/paper/2208.01740/) (2022)
- [Two-step dynamic obstacle avoidance](https://mlsanity.com/paper/2311.16841/) (2023)
- [Separation Assurance between Heterogeneous Fleets of Small Unmanned Aerial Systems via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.01041/) (2026)
- [Conflict Resolution under Degraded Surveillance in Air Corridors Using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2607.20547/) (2026)

## 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.