115 citations · 233 across the 11 of their papers we have counts for
11 papers
Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharing
Ting Li, Chengchun Shi, Zhaohua Lu +2
Many modern tech companies, such as Google, Uber, and Didi, utilize online experiments (also known as A/B testing) to evaluate new policies against existing ones. While most studie…
Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons
Chengchun Shi, Shikai Luo, Yuan Le +2
We consider reinforcement learning (RL) methods in offline domains without additional online data collection, such as mobile health applications. Most of existing policy optimizati…
Policy Evaluation for Temporal and/or Spatial Dependent Experiments
Shikai Luo, Ying Yang, Chengchun Shi +3
The aim of this paper is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial de…
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets
Chengchun Shi, Runzhe Wan, Ge Song +3
The two-sided markets such as ride-sharing companies often involve a group of subjects who are making sequential decisions across time and/or location. With the rapid development o…
A Deep Value-network Based Approach for Multi-Driver Order Dispatching
Xiaocheng Tang, Zhiwei Qin, Fan Zhang +5
Recent works on ride-sharing order dispatching have highlighted the importance of taking into account both the spatial and temporal dynamics in the dispatching process for improvin…
Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms
Xiaocheng Tang, Fan Zhang, Zhiwei Qin +6
Large ride-hailing platforms, such as DiDi, Uber and Lyft, connect tens of thousands of vehicles in a city to millions of ride demands throughout the day, providing great promises…
Reinforcement Learning for Ridesharing: An Extended Survey
Zhiwei Qin, Hongtu Zhu, Jieping Ye
In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to decision optimization problems in a typical ridesharing system.…
Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms
Fan Zhou, Shikai Luo, Xiaohu Qie +2
How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi. Such co…
Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning
Yan Jiao, Xiaocheng Tang, Zhiwei Qin +4
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-dem…
Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework
Chengchun Shi, Xiaoyu Wang, Shikai Luo +3
A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries. Major chall…
Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning
Zhenyu Shou, Xuan Di, Jieping Ye +3
Vacant taxi drivers' passenger seeking process in a road network generates additional vehicle miles traveled, adding congestion and pollution into the road network and the environm…