Reinforcement Learning Optimization Strategies for Dynamic Pricing and Inventory Control in E-commerce Retail

With the continuous development of Internet e-commerce, reasonable inventory arrangement for different warehouses or retailers and dynamic pricing of goods have gradually become key factors affecting the profitability of each company.The study proposes a reinforcement learning-based approach for dynamic pricing and inventory control in e-commerce retailing.The problem is modeled and converted into a Markov decision process by incorporating e-commerce retailing characteristics, and a joint inventory control and dynamic pricing algorithm for e-commerce retailing is designed based on the Deep Deterministic Policy Gradient (DDPG) method.The results of numerical experiments show that the joint inventory control and dynamic pricing strategies based on deep reinforcement learning have the best performance in terms of gains, with gains of 0.197 and 0.035, respectively.The numerical experiments validate the performance effectiveness of the algorithms proposed in this paper, and the DDPG algorithm significantly outperforms the traditional methods.This research can improve enterprise revenue and effectively promote the landing of reinforcement learning in the field of revenue management, which has practical application value.