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.

该研究通过将职业规划视为马尔可夫决策过程，并利用强化学习算法（如Sarsa、Q-Learning和A2C）分析荷兰职业市场的数据，开发了优化员工长期收入的策略。结果表明，相比观察到的职业路径，特别是Q-Learning和Sarsa模型可以使员工的收入轨迹平均增加5%。该研究承认了职位筛选的局限性、环境制定的简化以及对就业连续性和零申请成本的假设，并鼓励未来研究在收入优化之外探索其他目标并解决这些局限性，以进一步增强职业规划过程。

职业路径推荐：长期收入最大化的强化学习方法