Learning-Aided Heuristics Design for Storage System
  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.
