Advanced Deep Reinforcement Learning Framework for Dynamic Pricing Optimization in E-commerce Marketplaces

Pricing transparency is essential in online commerce to drive transactions and influence customer decisions. Dynamic pricing, a strategy to optimize sales and profit margins, remains crucial for online merchants. Questions/purposes: This study proposes a novel deep reinforcement learning (DRL) framework for dynamic pricing optimization in e-commerce, aiming to maximize revenue and profitability. Utilizing the Online Retail II Data Set, which includes diverse product categories and customer segments, we developed a DRL framework that adapts prices based on temporal data dynamics, market conditions, competitor actions, and customer preferences. The state space incorporates key features like product demand, inventory levels, competitor prices, and time-based variables. An agent is trained through a deep reinforcement learning architecture to learn optimal pricing strategies that balance long-term revenue and ethical considerations such as price fairness. Experimental comparisons with baseline models demonstrate the proposed framework’s superior performance in enhancing revenue and profitability. The data-driven and automated approach enables e-commerce businesses to respond flexibly to market changes, improving competitiveness and customer satisfaction. The proposed DRL framework represents a significant advancement in dynamic pricing strategies, offering a flexible, responsive, and autonomous approach to pricing optimization in the digital marketplace, ultimately enhancing revenue and profitability while maintaining ethical standards.