Automatic Decision Algorithm of Human Resource Management System Based on Reinforcement Learning

Human resource management (HRM) plays a key role in modern organizations, and its efficiency and effectiveness are crucial to the success of the organization. This study aims to explore and develop an automatic decision-making algorithm for HRM systems based on Reinforcement Learning (RL), in order to improve the quality and efficiency of HRM. This article proposes a dual DQN (DDQN) algorithm based on the Deep Q network (DQN) and dual Q-learning algorithms. Two independent DQN are introduced, which are used to select actions and evaluate the value of actions respectively. The parameters of these two networks are updated independently during the training process. At each time step, one Q-network is used to select an action, and the other is used to evaluate the value of the action. It is found that RL technology has great potential, which can meet various HRM challenges, including decision-making in employee recruitment, performance evaluation and training. The intelligent decision algorithm is expected to play a role in the field of HRM.