Reinforcement Learning for Dynamic Pricing and Demand Optimization in E-Commerce

Abstract

Dynamic pricing is a central mechanism through which e-commerce platforms balance revenue, demand, and customer engagement. Traditional pricing strategies rely on static rules or predictive models that struggle to adapt to rapidly changing market conditions. This study investigates the use of reinforcement learning for dynamic pricing and demand optimization in e-commerce environments. A learning-based pricing framework is proposed that continuously adapts pricing decisions based on observed demand responses and environmental feedback. Empirical evaluation demonstrates that reinforcement learning agents can improve revenue stability, demand alignment, and responsiveness compared to static and heuristic pricing approaches. The results highlight the practical potential of reinforcement learning as a decision support mechanism for modern digital commerce platforms.