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Nowadays, human resource is an important part of various resources ofenterprises. For enterprises, high-loyalty and high-quality talented personsare often the core competitiveness of enterprises. Therefore, it is of greatpractical significance to predict whether employees leave and reduce theturnover rate of employees. First, this paper established a multi-layerperceptron predictive model of employee turnover rate. A model based on Sarsawhich is a kind of reinforcement learning algorithm is proposed toautomatically generate a set of strategies to reduce the employee turnoverrate. These strategies are a collection of strategies that can reduce theemployee turnover rate the most and cost less from the perspective of theenterprise, and can be used as a reference plan for the enterprise to optimizethe employee system. The experimental results show that the algorithm canindeed improve the efficiency and accuracy of the specific strategy.

Obtain Employee Turnover Rate and Optimal Reduction Strategy Based On Neural Network and Reinforcement Learning