Design and Validation of Personalized Career Path Planning Algorithm Based on Deep Reinforcement Learning (DRL)

In career development planning, reconciling the differences between individual dynamics and environmental dynamics presents a challenge. This paper proposes a personalized career development planning algorithm based on deep reinforcement learning. The algorithm uses a Markov decision process to model career development, constructing a state space that integrates multi-dimensional features such as personal skills and market dynamics. Furthermore, a reward function is designed to replace short-term game theory and long-term goals. Borrowing from the framework of the DDPG algorithm, an Actor-Critic network is used to optimize the strategy in a high-dimensional continuous action space. The training stability is enhanced by combining experience replay and a target network mechanism. To improve personalized adaptability, a two-stage process of offline pre-training and online fine-tuning is proposed. The algorithm achieves a cumulative reward value of 589.4 and a goal achievement rate of 82.1%. The results show that the proposed algorithm can effectively generate dynamic personalized development paths, thereby promoting the sustainable achievement of individual career goals.