@inproceedings{Ye2025LICORICE,
author = {Zhuorui Ye and Stephanie Milani and Geoffrey J. Gordon and Fei Fang},
title = {LICORICE: Label-Efficient Concept-Based Interpretable Reinforcement Learning},
booktitle = {International Conference on Learning Representations (ICLR)},
year = {2025},
keywords = {reinforcement learning, interpretability, concept bottleneck models, label efficiency},
abstract = {We develop a training scheme that lets reinforcement learning agents learn concept-based interpretable policies while requiring only a small, labeled subset of concept annotations, making interpretable RL substantially more label-efficient.}
}