H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps

no code implementations • 22 Sep 2023 • Haoyi Niu, Tianying Ji, Bingqi Liu, Haocheng Zhao, Xiangyu Zhu, Jianying Zheng, Pengfei Huang, Guyue Zhou, Jianming Hu, Xianyuan Zhan

Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of offline data can be quite challenging.

Offline RL Reinforcement Learning (RL)

Automatic Background Filtering Method for Roadside LiDAR Data

no code implementations • Transportation Research Record, v 2672, n 45, p 106-114, 2018 • Jianqing Wu, Hao Xu, Yuan Sun, Jianying Zheng, and Rui Yue

Background filtering is the preprocessing step to obtain the HRMTD of different roadway users from roadside LiDAR data.