An entity-graph MARL framework (RACHE) using R-GCN message passing and attention pooling over train-service nodes outperforms baseline algorithms in railway pricing revenue across two simulated market scenarios.
Gutiérrez-Hita, O
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Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets
An entity-graph MARL framework (RACHE) using R-GCN message passing and attention pooling over train-service nodes outperforms baseline algorithms in railway pricing revenue across two simulated market scenarios.