Dynamic Regimes for Corporate Human Capital Development Used Reinforcement Learning Methods
Corporate human capital is a critical driver of sustainable economic growth, which is becoming increasingly important in the changing nature of work. Due to the expansion of various areas of human activity, the employee’s profile becomes multifaceted. Therefore, the problem of human capital manageme...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/18/3916 |
| _version_ | 1797578973085958144 |
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| author | Ekaterina V. Orlova |
| author_facet | Ekaterina V. Orlova |
| author_sort | Ekaterina V. Orlova |
| collection | DOAJ |
| description | Corporate human capital is a critical driver of sustainable economic growth, which is becoming increasingly important in the changing nature of work. Due to the expansion of various areas of human activity, the employee’s profile becomes 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 the corporate operational efficiency, is relevant, timely, socially, and economically significant. The paper proposes a methodology for the dynamic regimes for human capital development (DRHC) to design individual trajectories for the employee’s 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 DDQN, SARSA, and PRO algorithms to maximize the agent’s utility function. The implementation of the proposed DRHC policy would improve the quality of corporate human capital, increase labor resource efficiency, and ensure the productivity growth of companies. |
| first_indexed | 2024-03-10T22:30:15Z |
| format | Article |
| id | doaj.art-a733dd9735a64c5ba063d52878f736e9 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T22:30:15Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-a733dd9735a64c5ba063d52878f736e92023-11-19T11:49:22ZengMDPI AGMathematics2227-73902023-09-011118391610.3390/math11183916Dynamic Regimes for Corporate Human Capital Development Used Reinforcement Learning MethodsEkaterina V. Orlova0Department of Economics and Management, Ufa University of Science and Technology, 450076 Ufa, RussiaCorporate human capital is a critical driver of sustainable economic growth, which is becoming increasingly important in the changing nature of work. Due to the expansion of various areas of human activity, the employee’s profile becomes 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 the corporate operational efficiency, is relevant, timely, socially, and economically significant. The paper proposes a methodology for the dynamic regimes for human capital development (DRHC) to design individual trajectories for the employee’s 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 DDQN, SARSA, and PRO algorithms to maximize the agent’s utility function. The implementation of the proposed DRHC policy would improve the quality of corporate human capital, increase labor resource efficiency, and ensure the productivity growth of companies.https://www.mdpi.com/2227-7390/11/18/3916corporate human capitalindividual development trajectoriesmachine learningreinforcement learning<i>Q</i>-learning |
| spellingShingle | Ekaterina V. Orlova Dynamic Regimes for Corporate Human Capital Development Used Reinforcement Learning Methods Mathematics corporate human capital individual development trajectories machine learning reinforcement learning <i>Q</i>-learning |
| title | Dynamic Regimes for Corporate Human Capital Development Used Reinforcement Learning Methods |
| title_full | Dynamic Regimes for Corporate Human Capital Development Used Reinforcement Learning Methods |
| title_fullStr | Dynamic Regimes for Corporate Human Capital Development Used Reinforcement Learning Methods |
| title_full_unstemmed | Dynamic Regimes for Corporate Human Capital Development Used Reinforcement Learning Methods |
| title_short | Dynamic Regimes for Corporate Human Capital Development Used Reinforcement Learning Methods |
| title_sort | dynamic regimes for corporate human capital development used reinforcement learning methods |
| topic | corporate human capital individual development trajectories machine learning reinforcement learning <i>Q</i>-learning |
| url | https://www.mdpi.com/2227-7390/11/18/3916 |
| work_keys_str_mv | AT ekaterinavorlova dynamicregimesforcorporatehumancapitaldevelopmentusedreinforcementlearningmethods |