Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach

TL;DR

This paper applies reinforcement learning algorithms to simulate and optimize long-term career paths for income maximization, demonstrating significant income improvements over observed trajectories.

Contribution

It formulates career planning as an MDP and develops RL-based strategies for recommending high-income career paths, a novel application in this domain.

Findings

RL models achieved an average 5% income increase

Q-Learning and Sarsa outperformed other algorithms

The approach effectively simulates the Dutch job market environment

Abstract

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.