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.
翻译:存储系统等计算机系统通常需要透明的白箱算法,这些算法可以为人类专家解释。在这项工作中,我们提议了一种学习辅助的休养设计方法,自动产生深强化学习(DRL)代理商的人类可读战略。这种方法得益于深层学习的力量,但避免了黑箱属性的缺陷。除了白箱优势外,我们的存储生产资源配置设想中的实验还表明,这一解决方案优于系统默认设置和人类专家精心设计的手工艺战略。