no code implementations • 12 May 2017 • Zhou Xing, Eddy Baik, Yan Jiao, Nilesh Kulkarni, Chris Li, Gautam Muralidhar, Marzieh Parandehgheibi, Erik Reed, Abhishek Singhal, Fei Xiao, Chris Pouliot
These latent embeddings can be used either as features to feed to subsequent models, such as collaborative filtering, or to build similarity metrics between songs, or to classify music based on the labels for training such as genre, mood, sentiment, etc.
no code implementations • 27 May 2019 • Jiarui Jin, Ming Zhou, Wei-Nan Zhang, Minne Li, Zilong Guo, Zhiwei Qin, Yan Jiao, Xiaocheng Tang, Chenxi Wang, Jun Wang, Guobin Wu, Jieping Ye
How to optimally dispatch orders to vehicles and how to trade off between immediate and future returns are fundamental questions for a typical ride-hailing platform.
Multiagent Systems
no code implementations • 7 Oct 2019 • Ming Zhou, Jiarui Jin, Wei-Nan Zhang, Zhiwei Qin, Yan Jiao, Chenxi Wang, Guobin Wu, Yong Yu, Jieping Ye
Improving the efficiency of dispatching orders to vehicles is a research hotspot in online ride-hailing systems.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 25 Nov 2019 • John Holler, Risto Vuorio, Zhiwei Qin, Xiaocheng Tang, Yan Jiao, Tiancheng Jin, Satinder Singh, Chenxi Wang, Jieping Ye
Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform marketplace.
no code implementations • 8 Mar 2021 • Yan Jiao, Xiaocheng Tang, Zhiwei Qin, Shuaiji Li, Fan Zhang, Hongtu Zhu, Jieping Ye
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.