With the rising prevalence of smart mobile phones in daily life, online ride-hailing platforms have emerged as a viable solution to provide more timely and personalized transportation service, led by such companies as DiDi, Uber, and Lyft. These platforms also allow idle vehicle vacancy to be more effectively utilized to meet the growing need of on-demand transportation, by connecting potential mobility requests to eligible drivers.
Deep Reinforcement Learning in Ride-sharing Marketplace
Presented by Zhiwei (Tony) Qin, DiDi AI Labs
Wednesday, October 23, 2019
College Avenue Commons (CAVC) 359, Tempe campus [map]
In this talk, Qin will discuss the train of research on ride-hailing marketplace optimization at DiDi, in particular, order dispatching and driver repositioning. He will show single-agent and multi-agent RL formulations and how value function can be designed to leverage different amount of information and also facilitate knowledge transfer.
About the speaker
Zhiwei (Tony) Qin leads the reinforcement learning research at DiDi AI Labs, working on core problems in ride-sharing marketplace optimization. He received his PhD in operations research from Columbia University and bachelor’s of science in computer science and statistics from the University of British Columbia, Vancouver. Qin is broadly interested in research topics at the intersection of optimization and machine learning, and most recently in reinforcement learning and its applications in operational optimization, digital marketing, traffic signals control and education. He has published in top-tier conferences and journals in machine learning and optimization and served as PC of NeurIPS, AAAI, IJCAI and KDD. He received Best Demo Award in NeurIPS 2018 and holds more than 10 U.S. patents.