arXiv Machine Learning July 24, 2026 By Esrat Farhana Dulia, Syed Arbab Mohd Shihab, Caleb Adams, Ruben Del Rosario

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

Read the original on arXiv Machine Learning →

arXiv:2607. 20547v1 Announce Type: new Abstract: Safe Advanced Air Mobility operations require aircraft to maintain separation when surveillance information is noisy, delayed, incomplete, or temporarily unavailable.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 6

Integrated Noise and Safety Management in UAM via A Unified Reinforcement Learning Framework

arXiv:2508. 16440v2 Announce Type: replace-cross Abstract: Urban Air Mobility (UAM) envisions the widespread use of small aerial vehicles to transform transportation in dense urban environments.

By Surya Murthy, Zhenyu Gao, John-Paul Clarke, Ufuk Topcu

agents reinforcement-learning safety

arXiv Machine Learning

Jul 14

Runtime Safety Filtering for Learned Small UAS Separation Policies under GNSS Degradation

arXiv:2607. 10014v1 Announce Type: cross Abstract: Learning-based separation assurance for small Unmanned Aircraft Systems (sUAS) achieves near-zero collision rates in simulation, but assumes accurate position and velocity information from Global Navigation Satellite Systems (GNSS).

By Alex Zongo, Peng Wei

agents safety

arXiv Machine Learning

Jun 29

Training Observable Control Policies to Expose Agent State Through Actions

arXiv:2606. 27609v1 Announce Type: new Abstract: Physical or operational constraints often impose communications limitations on autonomous agents.

By Andres Enriquez Fernandez, John J. Bird

agents reinforcement-learning

arXiv AI

Jun 24

Decentralized Coordination of Autonomous Traffic Through Advanced Air Mobility Corridors

arXiv:2606. 23832v1 Announce Type: cross Abstract: The use of dedicated corridors for Advanced Air Mobility (AAM) traffic is one of the most commonly proposed pathways to integrating them into existing airspace operations.

By Jasmine Jerry Aloor, Hamsa Balakrishnan

agents

arXiv Machine Learning

Jul 21

Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials

arXiv:2105. 00990v3 Announce Type: replace Abstract: Autonomous control in high-dimensional, continuous state spaces is a persistent and important challenge in the fields of robotics and artificial intelligence.

By Adrian P. Pope, Jaime S. Ide, Daria Micovic, Henry Diaz, David Rosenbluth, Lee Ritholtz, Jason C. Twedt, Thayne T. Walker, Kevin Alcedo, Daniel Javorsek

agents reinforcement-learning robotics

arXiv AI

Jul 3

Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation

arXiv:2607. 01794v1 Announce Type: cross Abstract: With the rapid development of autonomous aerial systems, Unmanned Aerial Vehicles (UAVs) are increasingly deployed in applications such as inspection, environmental monitoring, and rescue, creating growing demand for reliable autonomous navigation.

By Shenghui Zhang, YuXuan Gao, Songwei Zhao, Jifeng Hu, Zijing Zhang, Hechang Chen

agents reinforcement-learning safety

Related stories

Integrated Noise and Safety Management in UAM via A Unified Reinforcement Learning Framework

Runtime Safety Filtering for Learned Small UAS Separation Policies under GNSS Degradation

Training Observable Control Policies to Expose Agent State Through Actions

Decentralized Coordination of Autonomous Traffic Through Advanced Air Mobility Corridors

Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials

Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation