23 citations · 32 across the 4 of their papers we have counts for
9 papers
Boosting Offline Reinforcement Learning with Residual Generative Modeling
Hua Wei, Deheng Ye, Zhao Liu +5
Offline reinforcement learning (RL) tries to learn the near-optimal policy with recorded offline experience without online exploration. Current offline RL research includes: 1) gen…
Learning to Simulate on Sparse Trajectory Data
Hua Wei, Chacha Chen, Chang Liu +2
Simulation of the real-world traffic can be used to help validate the transportation policies. A good simulator means the simulated traffic is similar to real-world traffic, which…
How Do We Move: Modeling Human Movement with System Dynamics
Hua Wei, Dongkuan Xu, Junjie Liang +1
Modeling how human moves in the space is useful for policy-making in transportation, public safety, and public health. Human movements can be viewed as a dynamic process that human…
A Probabilistic Simulator of Spatial Demand for Product Allocation
Porter Jenkins, Hua Wei, J. Stockton Jenkins +1
Connecting consumers with relevant products is a very important problem in both online and offline commerce. In physical retail, product placement is an effective way to connect co…
Learning Phase Competition for Traffic Signal Control
Guanjie Zheng, Yuanhao Xiong, Xinshi Zang +6
Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions. Among them, improving the urban transportat…
Diagnosing Reinforcement Learning for Traffic Signal Control
Guanjie Zheng, Xinshi Zang, Nan Xu +5
With the increasing availability of traffic data and advance of deep reinforcement learning techniques, there is an emerging trend of employing reinforcement learning (RL) for traf…