2024
RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences
ICML 2024spotlight
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 pre…