Researchers develop semi-Markov RL for EV ride-hailing, boosting profits and ensuring feasibility.

Researchers have developed a novel Semi-Markov Reinforcement Learning approach for managing large-scale electric vehicle ride-hailing fleets. This method ensures that dispatch, repositioning, and charging decisions strictly adhere to physical constraints like charger and feeder limits, even under uncertain demand and travel times. The system utilizes a masked actor to produce high-level intentions, which are then projected through a mixed-integer linear program to guarantee feasibility. Experiments on a New York City taxi dataset simulator demonstrated that this approach, named PD--RSAC, significantly outperformed baseline methods, achieving a net profit of $1.22 million while preventing any feeder-limit violations. AI

IMPACT Introduces a robust RL framework for complex fleet management, potentially improving operational efficiency and profitability in logistics.

RANK_REASON Academic paper detailing a new reinforcement learning method for a specific application.

Read on arXiv cs.AI →

• Graph Convolutional Network
• MADDPG
• MAPPO
• NYC
• PD--RSAC
• Semi-Markov Reinforcement Learning
• Soft Actor--Critic

• paper
• other

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