{
  "id": 194,
  "url": "https://arxiv.org/abs/2607.05179v1",
  "title": "Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets",
  "summary": "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. Consequently, agents must learn to infer strategic interactions only from observable market data which presents a significant challenge for multi-agent reinforcement learning,",
  "authors": "Enrique Adrian Villarrubia-Martin, David Muñoz-Valero, Luis Rodriguez-Benitez, Giovanni Montana, Luis Jimenez-Linares",
  "category": "research",
  "topics": "regulation,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-06T14:58:26.000Z",
  "fetched_at": "2026-07-14T14:14:19.972Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/194",
  "original_url": "https://arxiv.org/abs/2607.05179v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}