# Conflict Resolution under Degraded Surveillance in Air Corridors Using Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2607.20547 · HTML: https://mlsanity.com/paper/2607.20547/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations](https://mlsanity.com/paper/2003.08938/) (2020)
- [Reinforcement Learning with Random Delays](https://mlsanity.com/paper/2010.02966/) (2020)
- [Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/2111.14598/) (2021)
- [Conflict-Free Four-Dimensional Path Planning for Urban Air Mobility Considering Airspace Occupations](https://mlsanity.com/paper/2107.12829/) (2021)
- [Safe and Scalable Real-Time Trajectory Planning Framework for Urban Air Mobility](https://mlsanity.com/paper/2306.11647/) (2023)
- [Self-organized free-flight arrival for urban air mobility](https://mlsanity.com/paper/2404.03710/) (2024)
- [Data-Efficient Modeling for Precise Power Consumption Estimation of Quadrotor Operations Using Ensemble Learning](https://mlsanity.com/paper/2205.10997/) (2022)
- [Designing a Surveillance Sensor Network with Information Clearinghouse for Advanced Air Mobility](https://mlsanity.com/paper/2312.14787/) (2023)
- [An Integrated Supply Chain Network Design for Advanced Air Mobility Aircraft Manufacturing Using Stochastic Optimization](https://mlsanity.com/paper/2406.07766/) (2024)

## Abstract (arXiv)

Safe Advanced Air Mobility operations require aircraft to maintain separation when surveillance information is noisy, delayed, incomplete, or temporarily unavailable. This study develops a Deep Q-Network-based Multi-Agent Reinforcement Learning framework for decentralized conflict resolution among heterogeneous small unmanned aerial vehicles and electric vertical takeoff and landing aircraft operating within a structured three-dimensional corridor. Separate policies are trained for the two aircraft categories using local observations and a 14-action space that includes maintaining course, turning, vertical maneuvering, landing, and speed control. The simulation incorporates aircraft-specific dynamics, energy use, corridor constraints, observation noise, communication delay, information dropout, wind disturbance, actuator uncertainty, and model uncertainty. The trained policies are evaluated across 90 combinations of traffic density and minimum separation thresholds. Loss-of-separation frequency and duration generally increase with traffic density and separation requirements, although most events are resolved within 1s. Under safe conditions, agents maintain their motion approximately 79% of the time. During conflicts, turning accounts for 33% of actions, followed by maintaining motion at 29%, speed control at 25%, and vertical maneuvers at 13%. Six Pareto-optimal configurations reveal trade-offs between safety and corridor capacity. The framework supports the simulation-based evaluation of safer AAM conflict-resolution strategies under degraded surveillance conditions.