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

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,

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ethics.ai (6 July 2026), “Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets,” evidence record 194, https://ethics.ai/record/194 (originally published by arXiv).

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