Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach
It addresses career planning for employees in the Dutch job market, but it is incremental as it applies existing RL methods to a new domain with simplifications.
This study tackled the problem of optimizing long-term income through career planning by simulating the Dutch job market with reinforcement learning algorithms like Q-Learning and Sarsa, resulting in an average 5% income increase compared to observed paths.
This study explores the potential of reinforcement learning algorithms to enhance career planning processes. Leveraging data from Randstad The Netherlands, the study simulates the Dutch job market and develops strategies to optimize employees' long-term income. By formulating career planning as a Markov Decision Process (MDP) and utilizing machine learning algorithms such as Sarsa, Q-Learning, and A2C, we learn optimal policies that recommend career paths with high-income occupations and industries. The results demonstrate significant improvements in employees' income trajectories, with RL models, particularly Q-Learning and Sarsa, achieving an average increase of 5% compared to observed career paths. The study acknowledges limitations, including narrow job filtering, simplifications in the environment formulation, and assumptions regarding employment continuity and zero application costs. Future research can explore additional objectives beyond income optimization and address these limitations to further enhance career planning processes.