arXiv Machine Learning

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).

arXiv Machine Learning

Jul 24

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

By Esrat Farhana Dulia, Syed Arbab Mohd Shihab, Caleb Adams, Ruben Del Rosario

agents reinforcement-learning safety

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

arXiv AI

Jun 3

Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environments

arXiv:2506. 21129v2 Announce Type: replace-cross Abstract: Autonomous unmanned aerial vehicles (UAVs) increasingly rely on reinforcement learning (RL) for navigation.

By Deepak Kumar Panda, Adolfo Perrusquia, Weisi Guo

agents reinforcement-learning safety

arXiv AI

Jul 7

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control

arXiv:2509. 23960v2 Announce Type: replace-cross Abstract: Co-optimizing safety and performance in large-scale multi-agent systems remains a fundamental challenge.

By Manan Tayal, Aditya Singh, Shishir Kolathaya, Somil Bansal

agents reinforcement-learning benchmarks 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 Machine Learning

Aug 4

ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attacks

arXiv:2506. 22423v2 Announce Type: replace Abstract: Unmanned Aerial Vehicles (UAVs) depend on onboard sensors for perception, navigation, and control.

By Pritam Dash, Ethan Chan, Nathan P. Lawrence, Karthik Pattabiraman

reinforcement-learning robotics safety

arXiv Machine Learning

Aug 11

Satellite Trajectory Optimization via Proximal Policy Optimization for Space Debris Avoidance

arXiv:2608. 09628v1 Announce Type: new Abstract: Collision avoidance systems are commonly used to avoid fragmentation events occurring in Low-Earth Orbit (LEO) and Geosynchronous Equatorial Orbit (GEO).

By Logan Luna (Georgia Institute of Technology), Juan Ortiz Couder (Embry-Riddle Aeronautical University), Raul Alejandro Vargas-Acosta (Embry-Riddle Aeronautical University)

agents reinforcement-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 AI

Jun 8

Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach

arXiv:2510. 09041v3 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has demonstrated remarkable success in developing autonomous driving policies.

By Junchao Fan, Qi Wei, Ruichen Zhang, Yang Lu, Jianhua Wang, Xiaolin Chang, Bo Ai

agents reinforcement-learning robotics benchmarks safety

arXiv Machine Learning

Jul 10

Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning

arXiv:2607. 08137v1 Announce Type: cross Abstract: Federated reinforcement learning (FRL) is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhancing privacy and scalability in the decision-making process within dynamic vehicular environments.

By Zifan Zhang, Minghong Fang, Dianwei Chen, Zhuqing Liu, Prashant Khanduri, Xianfeng Yang, Anupam Das, Yuchen Liu

agents reinforcement-learning robotics safety

Hugging Face Trending Papers

Jun 25

Training Observable Control Policies to Expose Agent State Through Actions

Physical or operational constraints often impose communications limitations on autonomous agents. Such limitations complicate monitoring or multiagent coordination.

agents reinforcement-learning

arXiv AI

Jun 24

Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control

arXiv:2606. 24010v1 Announce Type: new Abstract: Multi-agent systems are widely used in safety-critical applications that require coordinated behavior under strict safety constraints.

By Zihao Guo, Jianing Zhao, Ling Li, Hao Liang, Giuseppe Loianno, Yali Du

agents reinforcement-learning safety

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