A Distributionally Robust Reinforcement Learning Framework for Constrained Urban EV Dispatch
We study city-scale control of electric-vehicle (EV) ride-hailing fleets where dispatch, repositioning, and charging decisions must respect charger and feeder limits under uncertain, spatially correlated demand and travel times. We formulate the problem as a hex-grid semi-Markov decision process (semi-MDP) with mixed actions -- discrete actions for serving, repositioning, and charging, together with continuous charging power -- and variable action durations. To guarantee physical feasibility during both training and deployment, the policy learns over high-level intentions produced by a masked, temperature-annealed actor. These intentions are projected at every decision step through a time...
| Authors | An Nguyen, Hoang Nguyen, Phuong Le et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 April 2026 |
| Last updated at source | 17 June 2026 |
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