InBEDE: Integrating Contextual Bandit with TD Learning for Joint Pricing and Dispatch of Ride-Hailing Platforms

@article{Chen2019InBEDEIC,
  title={InBEDE: Integrating Contextual Bandit with TD Learning for Joint Pricing and Dispatch of Ride-Hailing Platforms},
  author={Haipeng Chen and Yan Jiao and Zhiwei Qin and Xiaocheng Tang and Hao Li and Bo An and Hongtu Zhu and Jieping Ye},
  journal={2019 IEEE International Conference on Data Mining (ICDM)},
  year={2019},
  pages={61-70}
}
For both the traditional street-hailing taxi industry and the recently emerged on-line ride-hailing, it has been a major challenge to improve the ride-hailing marketplace efficiency due to spatio-temporal imbalance between the supply and demand, among other factors. Despite the numerous approaches to improve marketplace efficiency using pricing and dispatch strategies, they usually optimize pricing or dispatch separately. In this paper, we show that these two processes are in fact intrinsically… 

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