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