A multi-agent RL framework with graph neural networks lets ride-hailing vehicles jointly serve orders and collect fresh data for foundation model fine-tuning, improving a combined utility metric in simulation.
Optimizing long-term efficiency and fairness in ride-hailing via joint order dispatching and driver repositioning,
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Online Location Planning for AI-Defined Vehicles: Optimizing Joint Tasks of Order Serving and Spatio-Temporal Heterogeneous Model Fine-Tuning
A multi-agent RL framework with graph neural networks lets ride-hailing vehicles jointly serve orders and collect fresh data for foundation model fine-tuning, improving a combined utility metric in simulation.