Career Path Recommendations for Long-term Income Maximization: A
  Reinforcement Learning Approach
Spyros Avlonitis Dor Lavi Masoud Mansoury David Graus
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.