High-dimensional policies, such as those represented by neural networks, cannot be reasonably interpreted by humans. This lack of interpretability reduces the trust users have in policy behavior, limiting their use to low-impact tasks such as video games. Unfortunately, many methods rely on neural network representations for effective learning. In this work, we propose a method to build predictable policy trees as surrogates for policies such as neural networks. The policy trees are easily human interpretable and provide quantitative predictions of future behavior. We demonstrate the performance of this approach on several simulated tasks.
@article{arxiv.2109.08180,
title = {Interpretable Local Tree Surrogate Policies},
author = {John Mern and Sidhart Krishnan and Anil Yildiz and Kyle Hatch and Mykel J. Kochenderfer},
journal= {arXiv preprint arXiv:2109.08180},
year = {2021}
}
Comments
pre-print, submitted to AAAI 2022 Conference, 7 pages