English(EN) Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets

新框架使用图神经网络进行动态铁路定价

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-06 14:58

一篇新研究论文介绍了一种针对自由化高铁市场动态定价的新颖框架。该方法使用关系多智能体强化学习和图卷积网络来模拟市场拓扑结构，并从可观察数据中推断战略互动。该方法旨在通过考虑运营单元之间的竞争、协调和连通性来提高收入和稳定性，在复杂场景中优于现有基线。 AI

影响这项研究可能通过利用先进的强化学习技术，为交通运输行业带来更高效、更有利可图的定价策略。

排序理由该集群包含一篇详细介绍新研究方法的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.AI TIER_1 English(EN) · Enrique Adrian Villarrubia-Martin, David Mu\~noz-Valero, Luis Rodriguez-Benitez, Giovanni Montana, Luis Jimenez-Linares · 2026-07-07 04:00

Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets

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 c…
arXiv cs.AI TIER_1 English(EN) · Luis Jimenez-Linares · 2026-07-06 14:58

面向高铁市场的动态定价关系多智能体强化学习

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 betwee…