Titlebook: Reinforcement Learning in the Ridesharing Marketplace; Zhiwei (Tony) Qin,Xiaocheng Tang,Jieping Ye Book 2025 The Editor(s) (if applicable)

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Zhiwei (Tony) Qin,Xiaocheng Tang,Qingyang Li,Hongtu Zhu,Jieping Ye

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2690-4306 ase studies. The book also includes a review of two of methods closely related to reinforcement learning: approximate dynamic programming and model-predictive control. 978-3-031-59642-1978-3-031-59640-7Series ISSN 2690-4306 Series E-ISSN 2690-4314

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2690-4306 hat cover the optimization of ridesharing platforms using reThis book provides a comprehensive overview of reinforcement learning for ridesharing applications. The authors first lay out the fundamentals of the ridesharing system architectures and review the basics of reinforcement learning, includin

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Closing Remarks,nts, and the joint optimization of multiple levers. Along tackling these challenges, we expect that domain knowledge in ridesharing as well as transportation in general will be increasingly instrumental to the successful adoption of RL.

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Introduction, high as 41% vacant time for ridesharing vehicles in a large city (Brown 2020). The success of ridesharing, from the perspectives of the platforms, drivers, and passengers, requires sophisticated optimization of all the integrated components that collectively deliver the services.

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