IEEE RA-L

E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning

E2HiL: 基于熵引导的高效真实世界人机协同强化学习样本选择

Haoyuan Deng, Yudong Lin, Yuanjiang Xue, Haoyang Du, Qianzhun Wang, Boyang Zhou, Zhenyu Wu, Ziwei Wang

机构 * Nanyang Technological University, Singapore（南洋理工大学） ； Beijing University of Posts and Telecommunications（北京邮电大学） 纠错

AI总结 E2HiL通过熵引导的样本选择方法，提高了真实世界人机协同强化学习的样本效率和成功率，减少了人工干预需求。

Comments Project page: this https URL (https://e2hil.github.io/) Updated to the final IEEE RA-L version. The author list has been revised to match the published version, adding Yudong Lin and Qianzhun Wang. Main results and conclusions remain unchanged