Model for human capital management of an enterprise based on reinforcement learning methods

Human capital is an important driver for sustainable enterprise’s economic growth and becomes more important under digital transformation. The employee profile appears multifaceted due to the expansion of activities. Therefore, the problem of human capital management based on the design of employees’ individual trajectories of professional development is relevant, timely, socially and economically significant. The paper proposes a model for employees’ individual trajectories of the professional development, which is based on reinforcement learning methods. The model forms an optimal management regime and is considered as a consistent set of program activities aimed at the employee’s development in his professional sphere. It considers employee’s individual characteristics (health, competencies, motivation and social capital). The total control system is considered as a digital twin of an employee, and creates the environment — the model of an employee as a Markov decision process and the control model — the agent — a center of enterprise’s decision-making. We use reinforcement learning algorithms DDQN, SARSA, PRO to maximize the agent’s utility function. Based on the experiments, it is shown that the best results are provided by the DDQN algorithm. The results generated by the proposed model are of practical importance, which would contribute to the growth of an enterprise’s innovativeness and competitiveness by improving the human capital quality and increasing the labor resource efficiency.