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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.
This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to
Although there are many approaches to implement intrinsically motivated artificial agents, the combined usage of multiple intrinsic drives remains still a relatively unexplored research area. Specifically, we hypothesize that a mechanism capable of q
In real time electricity markets, the objective of generation companies while bidding is to maximize their profit. The strategies for learning optimal bidding have been formulated through game theoretical approaches and stochastic optimization proble
Model Order Reduction (MOR) methods enable the generation of real-time-capable digital twins, which can enable various novel value streams in industry. While traditional projection-based methods are robust and accurate for linear problems, incorporat
We establish a new connection between value and policy based reinforcement learning (RL) based on a relationship between softmax temporal value consistency and policy optimality under entropy regularization. Specifically, we show that softmax consist