The paper presents a graph-based framework for large-scale railway network management, combining a hierarchical Bayesian model with a Gaussian Process on a graph kernel to infer spatially correlated maintenance environments from Swiss Federal Railways data. It introduces a topology-aware Multi-Agent Reinforcement Learning system that uses graph neural networks and Transformers to optimize network-level policies. The approach demonstrates scalability via zero-shot transfer learning, enabling agents trained on small network segments to perform effectively on unseen large networks, outperforming heuristics and standard MARL baselines while reducing training time.
By Giacomo Arcieri, Gregory Duth\'e, Christophe Muller, Konstantinos G. Papakonstantinou, Daniel Straub, Eleni Chatzi
arXiv:2607. 05179v1 Announce Type: cross Abstract: In liberalised railway systems, operators must set prices dynamically in an environment with partial observability, as they retain private information about their objectives and performance, where regulatory constraints prohibit communication or direct information exchange between competitors to prevent explicit collusion.
By Enrique Adrian Villarrubia-Martin, David Mu\~noz-Valero, Luis Rodriguez-Benitez, Giovanni Montana, Luis Jimenez-Linares
The paper introduces Graph-Guided Quasimetric Dense Reward (G2QDR), a framework that learns a state connectivity model to predict pairwise connectivity strengths in asymmetric environments. These strengths are converted into scalar auxiliary dense rewards, offering continuous guidance across hierarchical levels. G2QDR can be integrated into any existing Goal-Conditioned Hierarchical Reinforcement Learning architecture and shows empirical performance improvements in sparse reward settings with modest computational cost.
By Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang, Doina Precup
arXiv:2511. 13103v2 Announce Type: replace Abstract: Multi-agent reinforcement learning (MARL) has shown promise for large-scale network control, yet existing methods face two major limitations.
By Vidur Sinha, Muhammed Ustaomeroglu, Guannan Qu
arXiv:2609.21945v1 Announce Type: new
Abstract: Urban transportation networks present complex optimization challenges spanning calibration of high-fidelity simulators and real-time operational contro...
By Adewumi Augustine Adepitan, Christopher J. Haruna, Oluwasegun Adegoke, Ayooluwatomiwa Ajiboye, Oluwatobi Oluwasakin
arXiv:2609.39777v1 Announce Type: new
Abstract: LLM-based multi-agent systems coordinate specialized reasoning through aggregation, interaction, and adaptive control, yet their potential for graph le...
By Jiayi Yang, Yifang Chen, Yuanfu Sun, Xinyan Ge, Qiaoyu Tan
arXiv:2507. 21873v2 Announce Type: replace Abstract: Graph neural networks (GNNs) excel at predictive tasks on graph-structured data but often lack the ability to incorporate symbolic domain knowledge and perform general reasoning.
By Raffaele Pojer, Andrea Passerini, Kim G. Larsen, Manfred Jaeger
arXiv:2601. 20753v4 Announce Type: replace Abstract: Preference-Conditioned Policy Learning (PCPL) in Multi-Objective Reinforcement Learning (MORL) approximates diverse Pareto-optimal solutions by conditioning a single policy on user-specified preferences, enabling run-time adaptation to arbitrary trade-offs without retraining.
By Zhiheng Jiang, Yunzhe Wang, Ryan Marr, Ellen Novoseller, Benjamin T. Files, Volkan Ustun
The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained by a spatial generalization gap.
arXiv:2508. 00429v5 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have achieved remarkable success in graph-based learning by propagating information among neighbor nodes via predefined aggregation mechanisms.
By Minghao Guo, Xi Zhu, Qingyue Jiao, Xiujin Liu, Haochen Xue, Chong Zhang, Shuhang Lin, Jingyuan Huang, Ziyi Ye, Yongfeng Zhang
arXiv:2509. 18930v3 Announce Type: replace-cross Abstract: Neural algorithmic reasoning (NAR) is a paradigm that trains neural networks to execute classic algorithms by supervised learning.
By Alex Schutz, Victor-Alexandru Darvariu, Efimia Panagiotaki, Bruno Lacerda, Nick Hawes
arXiv:2509. 12484v2 Announce Type: replace Abstract: We propose a novel neural network architecture, called Non-Trainable Modification (NTM), for computing Nash equilibria in stochastic differential games (SDGs) on graphs.
By Ruimeng Hu, Jihao Long, Haosheng Zhou