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.
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An adaptive digital-twin framework learns state-transition probabilities online via Dirichlet-Multinomial Bayesian updates and recomputes finite-horizon maintenance policies, demonstrated on a simulated railway bridge.
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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.
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Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics
An adaptive digital-twin framework learns state-transition probabilities online via Dirichlet-Multinomial Bayesian updates and recomputes finite-horizon maintenance policies, demonstrated on a simulated railway bridge.