arXiv AI

Lyapunov Stability-Aware Stackelberg Game for Low-Altitude Economy: A Control-Oriented Pruning-Based DRL Approach

arXiv:2602. 01131v2 Announce Type: replace Abstract: With the rapid expansion of the low-altitude economy, Unmanned Aerial Vehicles (UAVs) serve as pivotal aerial base stations supporting diverse services from users, ranging from latency-sensitive critical missions to bandwidth-intensive data streaming.

arXiv AI

Sep 18

Agentic AI Networking for Heterogeneous Unmanned Aerial Systems in Low-Altitude Wireless Networks

The paper introduces a hierarchical hybrid architecture combining large language models (LLMs) and multi-agent reinforcement learning (MARL) to manage heterogeneous unmanned aerial systems in low‑altitude wireless networks (LAWNs). An outer LLM‑driven loop interprets service requirements and operator intent to reconfigure objectives and resource priorities, while an inner MARL loop executes decentralized policies under the updated game. A logistics‑monitoring case study demonstrates the framework’s ability to coordinate diverse services and adapt to changing conditions without retraining the MARL policies.

By Nguyen Duc Minh Quang, Chang Liu, Shuangyang Li, Derrick Wing Kwan Ng

llms agents reinforcement-learning

arXiv AI

Aug 6

Joint UAV Flight and Opportunistic Routing under Reinforcement Learning for Delay-Tolerant Networks

arXiv:2608. 04590v1 Announce Type: new Abstract: The growing deployment of delay-tolerant networks (DTNs) has made store-carry-forward (SCF) communication indispensable under sparse connectivity.

By Xiao Wang, Shun-Ren Yang

reinforcement-learning

arXiv AI

Jun 9

Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization

arXiv:2510. 10028v2 Announce Type: replace-cross Abstract: The rapid advancement of Low-Altitude Economy Networks (LAENets) has enabled a variety of applications, including aerial surveillance, environmental sensing, and semantic data collection.

By Yang Li, Ruichen Zhang, Yinqiu Liu, Guangyuan Liu, Abbas Jamalipour, Xianbin Wang, Dong In Kim

llms nlp reinforcement-learning multimodal

arXiv Machine Learning

Jun 16

Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networks

arXiv:2606. 16331v1 Announce Type: new Abstract: The integration of generative artificial intelligence with wireless communication and signal processing systems has opened new avenues for intelligent, data-driven decision-making in future 6G networks.

By Eslam Eldeeb, Hirley Alves

diffusion reinforcement-learning efficiency safety

arXiv Machine Learning

Aug 4

MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks

arXiv:2608. 01128v1 Announce Type: cross Abstract: In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks.

By Yixin Zhang, Zhuohui Yao, Wenchi Cheng, Walid Saad

agents reinforcement-learning

arXiv AI

Aug 18

An Agentic AI Framework with Large Language Models and Chain-of-Thought for UAV-Assisted Logistics Scheduling with Mobile Edge Computing

arXiv:2605. 13221v2 Announce Type: replace Abstract: In cloud manufacturing, unmanned aerial vehicles (UAVs) can support both product collection and mobile edge computing (MEC).

By Hanwen Zhang, Dusit Niyato, Wei Zhang, Xin Lou, Malcolm Yoke Hean Low

llms rag agents reinforcement-learning

arXiv Machine Learning

Aug 27

AERIS: Offline Policy Improvement for Multi-UAV Integrated Sensing and Communication

AERIS is an offline policy improvement framework for multi-UAV integrated sensing and communication (ISAC) that learns from fixed flight logs using centralized training and decentralized execution. It introduces STAR-CRDT, an offline multi-agent RL algorithm that rectifies local actions and distills trusted improvements into decentralized actors, providing an offline-support policy improvement guarantee. Experiments demonstrate that STAR-CRDT boosts the main ISAC objective return by 29.3% and improves communication sum rate, sensing pass rate, and sensing margin while reducing collision-risk events by 54.2%.

By Ziyuan Wang (Steven), Yifan Sui (Steven), Wei Wei (Steven), Wenjie Xin (Steven), Zekai Zhang (Steven), Xiangwang Hou (Steven), Xiao-Ping (Steven), Zhang

agents reinforcement-learning safety

arXiv Machine Learning

Aug 21

Learn for Variation: Efficient AAV Trajectory Learning through a Differentiable Wireless World Model

arXiv:2603. 18853v3 Announce Type: replace-cross Abstract: Autonomous aerial vehicles (AAVs) enable data collection for sixth-generation Internet-of-Things networks, but their trajectories couple nonlinear wireless rates with long-horizon service progress.

By Xiucheng Wang, Zhenye Chen, Nan Cheng, Zhisheng Yin, Xuemin Shen

agents

arXiv Machine Learning

Jul 15

Environment Parameter Gradient Theorem for Policy-Environment Co-Design in Reinforcement Learning

arXiv:2607. 12590v1 Announce Type: cross Abstract: Reinforcement learning (RL) is traditionally concerned with learning a control policy for a fixed environment.

By Amber Srivastava

agents reinforcement-learning

arXiv Machine Learning

Sep 10

Environment Parameter Gradient Theorem for Co-Design in Reinforcement Learning

arXiv:2607.12590v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) is traditionally concerned with learning a control policy for a fixed environment. In many engineering systems, h...

By Amber Srivastava

agents reinforcement-learning

arXiv Machine Learning

Jul 22

Reinforcement Learning for Delivery Drone-Based Participatory Sensing in Dynamic Environments

arXiv:2607. 18874v1 Announce Type: new Abstract: Using Unmanned Aerial Vehicle (UAV) for urban sensing has emerged as a powerful paradigm to monitor the status of the city, e.

By Xin Ouyang, Songxin Lei, Xusen Guo, Yutian Jiang, Sijie Ruan, Yuxuan Liang

rag reinforcement-learning robotics

arXiv AI

Jun 2

Digital Twin-Assisted Adaptive Multi-Agent DRL for Intelligent Spectrum and Resource Management in Open-RAN UAV-Enabled 6G Networks

arXiv:2606. 01324v1 Announce Type: cross Abstract: The evolution toward 6G wireless networks envisions a seamlessly intelligent, Open-RAN-enabled architecture where unmanned aerial vehicles (UAVs) play a pivotal role in extending coverage, enhancing resilience, and ensuring reliable connectivity for ground users deployment.

By Marwan Dhuheir, Thang X. Vu, Symeon Chatzinotas

agents reinforcement-learning

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