Reinforcement Learning in Dynamic Pricing Models for E-Commerce

Patel, Dip Bharatbhai

The Es Economics and Entrepreneurship
Vol. 1 No. 01 (2022): The ES Economics And Entrepreneurship (ESEE )

Dynamic pricing has revolutionized the e-commerce industry by enabling businesses to adapt prices in real time to maximize revenue and customer satisfaction. This paper explores the application of reinforcement learning (RL) in dynamic pricing models, highlighting how RL can optimize pricing strategies by learning from historical and real-time data. The discussion includes an overview of traditional dynamic pricing methods, the advantages of RL in this context, implementation challenges, and real-world applications. The findings suggest that RL offers significant potential for improving pricing efficiency, enhancing customer experience, and driving competitive advantages in e-commerce.