论文标题
可解释的基于马尔可夫的可解释的基于模型的深钢筋学习学习层次结构框架，用于预测涡轮扇击发动机
Interpretable Hidden Markov Model-Based Deep Reinforcement Learning Hierarchical Framework for Predictive Maintenance of Turbofan Engines
论文作者
论文摘要
深度强化学习中的一个开放研究问题是如何将稀疏领域中关键决策的政策学习集中在政策中。本文强调，将隐藏的马尔可夫模型和强化学习的优势结合起来，朝着可解释的维护决策中。我们提出了一种新型的层次建模方法，该方法在高水平上检测并解释了失败的根本原因以及Turbofan Engine的健康降解，而在低水平上，它提供了最佳的替换政策。它的表现优于直接应用于原始数据的深钢筋学习方法的基线性能，或者使用隐藏的马尔可夫模型而没有这样的专业层次结构。但是，它还提供了与先前的工作相当的绩效，但具有可解释性的额外好处。
An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain. This paper emphasizes combining the advantages of inputoutput hidden Markov models and reinforcement learning towards interpretable maintenance decisions. We propose a novel hierarchical-modeling methodology that, at a high level, detects and interprets the root cause of a failure as well as the health degradation of the turbofan engine, while, at a low level, it provides the optimal replacement policy. It outperforms the baseline performance of deep reinforcement learning methods applied directly to the raw data or when using a hidden Markov model without such a specialized hierarchy. It also provides comparable performance to prior work, however, with the additional benefit of interpretability.
