论文标题
基于神经网络和强化学习，获得员工离职率和最佳减少策略
Obtain Employee Turnover Rate and Optimal Reduction Strategy Based On Neural Network and Reinforcement Learning
论文作者
论文摘要
如今，人力资源是企业各种资源的重要组成部分。对于企业而言，高级和高质量的才华横溢的人通常是企业的核心竞争力。因此，预测员工是否离开并降低员工的离职率是非常重要的。首先，本文建立了员工周转率的多层知觉预测模型。提出了一种基于SARSA的模型，该模型是一种强化学习算法，以自动生成一组策略来降低员工的离职率。这些策略是一系列策略的集合，可以降低员工离职率最高，而从企业的角度来看，成本较低，可以用作企业的参考计划来优化员工系统。实验结果表明，该算法确实可以提高特定策略的效率和准确性。
Nowadays, human resource is an important part of various resources of enterprises. For enterprises, high-loyalty and high-quality talented persons are often the core competitiveness of enterprises. Therefore, it is of great practical significance to predict whether employees leave and reduce the turnover rate of employees. First, this paper established a multi-layer perceptron predictive model of employee turnover rate. A model based on Sarsa which is a kind of reinforcement learning algorithm is proposed to automatically generate a set of strategies to reduce the employee turnover rate. These strategies are a collection of strategies that can reduce the employee turnover rate the most and cost less from the perspective of the enterprise, and can be used as a reference plan for the enterprise to optimize the employee system. The experimental results show that the algorithm can indeed improve the efficiency and accuracy of the specific strategy.
