Reinforcement Learning for Employee Performance Forecasting and Optimization

Abstract

In this work, an innovative hybrid deep reinforcement learning architecture is proposed that includes feedback for continuous adaptation in order to predict and enhance employee performance in volatile environments. The model utilizes structured HR data and qualitative feedback to provide a deeper understanding of complex relationships between human resource key performance indicators (HRKPIs). A two-stage approach is employed: first, Recursive Feature Elimination (RFE) is used to identify key drivers of performance, after which a Deep Q-Network (DQN) agent, which has been trained using Ray RLlib, predicts future performance states and provides intelligent adjustments. Productivity, peer feedback, and task efficiency are among the multidimensional indicators applied for the description of the reward function. The application of human-in-the-loop approach adds model transparency to allow for improved policy adjustment. In the real world, trials demonstrate that the proposed approach can surpass traditional static models in the aspect of accuracy and adaptability to different workforce situations. With the application of advanced AI methods this research offers a scalable, interpretable, and data driven solution to optimize workforce performance for organizations.