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Q-learning Approach in Ship Safety Inspection Data

机译：船舶安全检查数据的Q学习方法

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摘要

In the study, we focus the inspections of deficiencies basing 2000 - 2014 database of Tokyo MOU, and a Q-learning method to check the risk decision-making model. The input data are inspections of deficiencies ratio, current occurrences of deficiencies, and occurrences of deficiencies in the next 5 years, depending on the weight to feedback R value, and execute Q-learning algorithm and Markov chain to correct Q-Table, and the risk of changes of the vessels basing different Flag State and Classification Societies are obtained. The results of study, Port State Control would pay effort on the priority of the vessels in future year, as the ship's safety status will turn into high-risk in forecasting years. Port State Control would pay effort on the priority of the vessels of the vessels basing different Flag State and Classification Societies to reduce the risk.
机译：在这项研究中，我们重点研究了基于东京谅解备忘录的2000-2014年数据库的缺陷，并采用了一种Q学习方法来检查风险决策模型。输入数据是检查缺陷率，缺陷的当前发生率以及未来5年中的缺陷发生情况的检查，具体取决于反馈R值的权重，并执行Q学习算法和Markov链来校正Q表，并且获得了基于不同船旗国和船级社的船只变化的风险。研究结果显示，港口国控制局将在未来的一年中为船舶的优先级做出努力，因为在预测年内船舶的安全状况将变成高风险。港口国控制局将根据不同船旗国和船级社的优先权，努力降低风险。

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