A Deep Q-Learning Optimization Framework for Dynamic Pricing in E-Commerce

A Regarding the research on dynamic pricing optimization of e-commerce, traditional pricing methods are too poorly adaptable and not flexible enough when dealing with rapidly changing markets and user behaviors. To solve this problem, we proposed a dynamic pricing model based on deep Q learning. The core of this model is to use reinforcement learning to simulate the interaction between intelligent agents and the market, and learn the optimal pricing strategy through continuous trial and error. The model input includes multi-dimensional data such as market demand, user behavior, and inventory levels. Deep neural networks are used to approximate the state-action value function to achieve intelligent optimization of pricing. We conducted experiments on actual transaction data of a Chinese e-commerce platform, and the results are very obvious: in the three scenarios of stable transaction period, promotion peak period, and inventory backlog period, profits increased by 12.8% to 18.4%, user conversion rate increased by 8.5% to 12.7%, and inventory turnover rate was optimized by 15.2% to 23%, which are significantly better than traditional rule-based pricing methods. This model can dynamically adjust prices in real time according to market changes, while taking into account profitability and user experience. The main innovation of this work is to introduce deep Q learning into the e-commerce pricing problem and verify its effectiveness in a complex and high-dimensional market environment. This provides a new method for intelligent pricing in e-commerce and also opens up a new path for the application of reinforcement learning in commercial scenarios.