arXiv AI July 7, 2026 By Siyuan Wang, Lei Lei, Pranav Maheshwari, Sam Bellefeuille, Kan Zheng

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking

arXiv:2603. 06607v2 Announce Type: replace-cross Abstract: Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications.

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

arXiv Machine Learning

Jun 9

A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles

arXiv:2508. 00917v2 Announce Type: replace-cross Abstract: Connected autonomous vehicles (CAVs) must simultaneously perform multiple tasks, such as perception, prediction, planning, and control, to ensure safe and reliable navigation in complex environments.

By Jiayuan Wang, Farhad Pourpanah, Q. M. Jonathan Wu, Ning Zhang

agents

arXiv AI

Jun 4

Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers

arXiv:2606. 04328v1 Announce Type: cross Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM.

By Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci

llms agents reinforcement-learning fine-tuning

arXiv AI

Jul 24

Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections

arXiv:2607. 21488v1 Announce Type: cross Abstract: Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning (MARL) systems, which typically struggle with combinatorial action spaces, reliance on privileged information, or rigid agent designs.

By Gil Lifshits, Igal Bilik, Gilad Katz

rag agents reinforcement-learning benchmarks

arXiv AI

Jun 19

Oranits: Mission Assignment and Task Offloading in Open RAN-based ITS using Metaheuristic and Deep Reinforcement Learning

arXiv:2507. 19712v3 Announce Type: replace-cross Abstract: In this paper, we explore mission assignment and task offloading in an Open Radio Access Network (Open RAN)-based intelligent transportation system (ITS), where autonomous vehicles leverage mobile edge computing for efficient processing.

By Ngoc Hung Nguyen, Nguyen Van Thieu, Quang-Trung Luu, Anh Tuan Nguyen, Senura Wanasekara, Nguyen Cong Luong, Fatemeh Kavehmadavani, Van-Dinh Nguyen

agents reinforcement-learning

arXiv AI

Jul 24

AI-Driven Multi-Hop Relay Selection for Smart Urban NR-V2X Networks via Learning-to-Optimize Graph Neural Networks

arXiv:2607. 20554v1 Announce Type: new Abstract: Reliable and low-latency NR-V2X communications are essential for smart mobility in dense urban environments.

By Giambattista Amati, Federica Mangiatordi, Simone Angelini, Emiliano Pallotti, Pierpaolo Salvo

efficiency

arXiv AI

Jul 15

TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale

arXiv:2607. 13028v1 Announce Type: cross Abstract: Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground behavior in real-world map structure, and diverse enough to cover the safety-critical long tail that logged data rarely contains.

By Zhouchonghao Wu, Akshay Rangesh, Weixin Li, Wei-Jer Chang, Zachary Lee, Tim Wang, Wei Zhan

agents reinforcement-learning robotics benchmarks safety

Related stories

A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles

Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers

Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections

Oranits: Mission Assignment and Task Offloading in Open RAN-based ITS using Metaheuristic and Deep Reinforcement Learning

AI-Driven Multi-Hop Relay Selection for Smart Urban NR-V2X Networks via Learning-to-Optimize Graph Neural Networks

TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale