Reinforcement Learning Methods and Algorithms for Modeling Individual Trajectories of Professional Development

Abstract

Corporate human capital is one of the most important drivers of sustainable economic growth that is becoming increasingly important under changing nature of work. Due to the expansion of various areas of human activity, an employee portrait is getting multifaceted. Therefore, the problem of human capital management based on the individual trajectories of professional development, aimed at increasing the labor efficiency and contributing to the growth of he corporate operational efficiency, is relevant, timely, socially and economically significant. The paper proposes an approach for the dynamic regimes for human capital development (DRHC), to design individual trajectories for the employees professional development, based on reinforcement learning methods. The DRHC develops an optimal management regime as a set of programs aimed at developing an employee in the professional field, taking into account their individual characteristics (health quality, major and interdisciplinary competencies, motivation and social capital). The DRHC architecture consists of an environment — an employee model as a Markov decision making process and an agent — decision making center of a company. The DRHC uses Double Deep Q-Network algorithms to maximize the agents utility function. The implementation of the proposed by DRHC policies would enhance the corporate HC quality, improve its resource efficiency and ensure the performance growth