Preference-based Reinforcement Learning (PbRL) circumvents the need for reward engineering by harnessing human preferences as the reward signal. However, current PbRL methods excessively depend on high-quality feedback from domain experts, which results in a lack of robustness. In this paper, we present RIME, a robust PbRL algorithm for effective reward learning from noisy preferences. Our method utilizes a sample selection-based discriminator to dynamically filter out noise and ensure robust training. To counteract the cumulative error stemming from incorrect selection, we suggest a warm start for the reward model, which additionally bridges the performance gap during the transition from pre-training to online training in PbRL. Our experiments on robotic manipulation and locomotion tasks demonstrate that RIME significantly enhances the robustness of the state-of-the-art PbRL method. Code is available at https://github.com/CJReinforce/RIME_ICML2024.
@article{arxiv.2402.17257,
title = {RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences},
author = {Jie Cheng and Gang Xiong and Xingyuan Dai and Qinghai Miao and Yisheng Lv and Fei-Yue Wang},
journal= {arXiv preprint arXiv:2402.17257},
year = {2024}
}