arXiv AI

Learning-Assisted Congestion-Aware Route Scheduling for Semiconductor Fab Material Control Systems

arXiv AI

Jun 3

AUGUSTE: Online-Learning dApp for Predictive URLLC Scheduling

arXiv:2606. 03664v1 Announce Type: cross Abstract: Ultra Reliable and Low Latency Communications (URLLC) was one of the main motivations behind 5G, with 3GPP advertising 1-10 ms latency targets for applications such as industrial automation, Vehicle-To-Everything (V2X), tactical edge networking, and unmanned-system control.

By Maxime Elkael, Michele Polese, Yunseong Lee, Koichiro Furueda, Tommaso Melodia

agents

arXiv Machine Learning

Jul 28

Learning to Optimize: Joint Routing and Flow Allocation on Sparse Non-Euclidean Networks

arXiv:2607. 23467v1 Announce Type: new Abstract: We study an integrated pickup-and-delivery problem on sparse, non-Euclidean networks that jointly optimizes cyclic routing, cargo flow allocation, and cross-cycle service.

By Haomiao Sun, Fang He, Congyuan Ji, Xindi Tang

reinforcement-learning benchmarks

arXiv AI

Sep 4

From Prior-Guided Heuristics to Deployable Agents: Accelerating Demonstration-Driven Reinforcement Learning for Deadline-Constrained Network Control

The paper proposes a deployment‑focused framework for deadline‑constrained network control, introducing the Effective Congestion (EC) metric family and Uniform Path Grouping (UPG) heuristic to better capture traffic urgency and balance load. It integrates these with a Multi‑Agent Deep Reinforcement Learning architecture (MADRL EC (p*)) that combines a distributed scheduler and a centralized RL router. A unified training objective merges live‑reward, pre‑collected‑reward, and policy‑imitation terms, leading to the Model‑Guided Annealed Reinforcement Learning (MGA‑RL) protocol built on DDPG, which generalizes offline‑to‑online learning for demonstration‑driven training.

By Vincenzo Norman Vitale, Mohammad Solki, Antonia Maria Tulino, Andreas F. Molisch, Jaime Llorca

agents reinforcement-learning

arXiv AI

Jul 21

A Deep Reinforcement Learning Algorithm for the Vehicle Routing Problem with Stochastic Demands and Outsourcing

arXiv:2607. 16875v1 Announce Type: cross Abstract: We introduce the vehicle routing problem with stochastic demands and outsourcing options (VRP-SDO), in which a logistics service provider partitions customer requests into customers outsourced to a common carrier and customers committed to its fixed fleet.

By Mohsen Dastpak, Fausto Errico, Ola Jabali

reinforcement-learning fine-tuning benchmarks

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 Machine Learning

Aug 4

Learning Graph-Indexed Trajectory Patterns for Stochastic On-Time Arrival Routing

arXiv:2508. 17218v4 Announce Type: replace Abstract: Correlated link travel times create decision-relevant patterns in partial route histories.

By Yuanhang Wang, Xing Wei, Duoxiang Zhao, Zezhou Zhang, Hao Qin, Yuqi Ouyang

llms benchmarks

arXiv AI

Aug 7

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks

arXiv:2608. 05340v1 Announce Type: cross Abstract: Time-Sensitive Networking (TSN) and Mobile Edge Computing (MEC) hold strong potential for enabling ultra-reliable low-latency communication for time-sensitive applications, such as eXtended Reality (XR).

By Marcos Carvalho, Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci, Daniel F. Macedo

llms agents reinforcement-learning

arXiv AI

Sep 15

Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning

arXiv:2609.15763v1 Announce Type: cross Abstract: Federated continual learning (FCL) enables shared global models to continuously adapt to distributed and non-stationary data streams, making it impor...

By Yuxuan Sun, Yuxuan Bai, Tan Chen, Sheng Zhou, Zhisheng Niu

arXiv Machine Learning

Aug 13

Grounding Large Language Models as Generalizable Policies in Network Control

arXiv:2512. 11839v2 Announce Type: replace Abstract: Designing generalizable control policies that operate reliably under changing conditions is essential for robust network services in modern digital infrastructure.

By Duo Wu, Linjia Kang, Zhimin Wang, Fangxin Wang, Wei Zhang, Chongbo Sun, Xuefeng Tao, Wei Yang, Le Zhang, Wenwu Zhu, Peng Cui, Zhi Wang

llms safety

arXiv Machine Learning

Sep 1

Conservative Hybrid Graph Networks for Process Systems with Learned Routing

arXiv:2608.28896v1 Announce Type: new Abstract: Industrial process networks do not maintain a single effective topology while operating: streams are throttled or bypassed, and units move between idle...

By Paolo Guida

arXiv AI

Sep 1

Trajectory-Initialized Neural Double Q-Routing for Large-Scale Overhead Hoist Transport Systems

arXiv:2608.30512v1 Announce Type: cross Abstract: Large-scale industrial robot fleets share constrained physical infrastructure, making vehicle travel times dependent on safety separation, intersecti...

By Cheng Gu, Qiusheng Zhao, Anbang Liu, Shaochong Lin, Max Z. J. Shen

robotics safety

arXiv AI

Jul 7

Adaptive Inference Batching using Policy Gradients

arXiv:2607. 05272v1 Announce Type: cross Abstract: Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching policies that require manual tuning and cannot adapt to shifting traffic.

By Ruslan Sharifullin

agents reinforcement-learning efficiency

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