Leveraging Reinforcement Learning and Bayesian Optimization for Dynamic Pricing Strategies in E-commerce

Abstract

In the rapidly evolving landscape of e-commerce, dynamic pricing strategies have become crucial for maximizing revenue and maintaining competitive advantage. This paper explores the integration of reinforcement learning (RL) and Bayesian optimization as a novel approach to dynamic pricing. Reinforcement learning offers the capability to adaptively learn pricing policies from complex, non-stationary environments, allowing e-commerce platforms to respond in real-time to market fluctuations and consumer behavior. Bayesian optimization, on the other hand, provides a probabilistic model-based method for efficiently sampling and optimizing the price space, thus enhancing the exploration-exploitation trade-off crucial for RL's convergence and performance. Our proposed framework harnesses the strengths of both methods, where Bayesian optimization guides the reward model tuning in RL, resulting in faster convergence and improved pricing strategies. We empirically validate this approach through extensive simulations and real-world data experiments from a leading e-commerce retailer. The results demonstrate that our hybrid model significantly outperforms traditional pricing algorithms, yielding a 15% increase in revenue and a 12% improvement in customer satisfaction metrics. Furthermore, we present a detailed analysis of the scalability and computational efficiency of the proposed solution, highlighting its practical implications for large-scale e-commerce applications. The findings underscore the potential of leveraging advanced machine learning techniques for developing robust and adaptive pricing strategies, setting a foundation for future innovations in intelligent e-commerce systems.