New DRL Framework Optimizes Urban EV Fleet Control

Researchers have developed a new framework for controlling urban electric vehicle (EV) fleets that uses distributionally robust reinforcement learning (DRL) to handle uncertain demand and travel times. This approach, called PD-RSAC, optimizes dispatch, repositioning, and charging decisions while strictly adhering to charger and feeder capacity limits. Experiments using New York City taxi data demonstrated that PD-RSAC significantly increased net profit to $1.22 million, outperforming various heuristic and reinforcement learning baselines. AI

IMPACT This framework could improve the efficiency and profitability of large-scale urban mobility services by optimizing EV fleet operations under uncertainty.

RANK_REASON Research paper detailing a new reinforcement learning framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• graph convolutional network
• Greedy
• MADDPG
• Mappō
• New York City
• PD--RSAC
• Soft Actor--Critic
• The An Nguyen
• Wasserstein-1

• paper
• other

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