Dynamic Pricing Model for E-Commerce Products Based on DDQN

This' paper addresses the challenges of intense price competition, price elasticity, and significant demand fluctuations in e-commerce product markets by adopting a dynamic pricing approach. Focusing on a product from the JD.com e-commerce platform, historical data spanning the past three years are analyzed, considering factors such as shipping costs, product inventory, product costs, and the impact of holidays. The study employs the Double Deep Q-Network (DDQN) for dynamic pricing optimization of the product, and compares its performance with the Deep Q-Network (DQN) model. The results indicate that both the DQN algorithm and DDQN algorithm lead to varying degrees of profit improvement for the dynamic pricing of products. Specifically, the pricing profit with the DQN algorithm increased by an average of 1.925% compared with the original pricing profit, while the pricing profit with the DDQN algorithm increased by an average of 11.975% compared with the original pricing profit. These findings demonstrate practical significance.