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• W3136804670 abstract "We present a new practical framework based on deep reinforcement learning and decision-time planning for real-world vehicle repositioning on ride-hailing (a type of mobility-on-demand, MoD) platforms. Our approach learns the spatiotemporal state-value function using a batch training algorithm with deep value networks. The optimal repositioning action is generated on-demand through value-based policy search, which combines planning and bootstrapping with the value networks. For the large-fleet problems, we develop several algorithmic features that we incorporate into our framework and that we demonstrate to induce coordination among the algorithmically-guided vehicles. We benchmark our algorithm with baselines in a ride-hailing simulation environment to demonstrate its superiority in improving income efficiency meausred by income-per-hour. We have also designed and run a real-world experiment program with regular drivers on a major ride-hailing platform. We have observed significantly positive results on key metrics comparing our method with experienced drivers who performed idle-time repositioning based on their own expertise." @default.
• W3136804670 created "2021-03-29" @default.
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• W3136804670 date "2021-03-08" @default.
• W3136804670 modified "2023-09-22" @default.
• W3136804670 title "Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning" @default.
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• W3136804670 doi "https://doi.org/10.48550/arxiv.2103.04555" @default.
• W3136804670 hasPublicationYear "2021" @default.
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