Computer systems such as storage systems normally require transparent white-box algorithms that are interpretable for human experts. In this work, we propose a learning-aided heuristic design method, which automatically generates human-readable strategies from Deep Reinforcement Learning (DRL) agents. This method benefits from the power of deep learning but avoids the shortcoming of its black-box property. Besides the white-box advantage, experiments in our storage productions resource allocation scenario also show that this solution outperforms the systems default settings and the elaborately handcrafted strategy by human experts.
@article{arxiv.2106.07288,
title = {Learning-Aided Heuristics Design for Storage System},
author = {Yingtian Tang and Han Lu and Xijun Li and Lei Chen and Mingxuan Yuan and Jia Zeng},
journal= {arXiv preprint arXiv:2106.07288},
year = {2021}
}