Reinforcement Learning for Dynamic Pricing under Competition for Perishable Products

Dynamic pricing strategies, which adapt prices based on external influences, have gained prominence in various industries. This approach involves frequent adjustments in response to factors like demand fluctuations, market trends, competitor pricing strategies and product perishability. However, accurately predicting demand and modeling price-environment interactions remains a significant challenge. In this paper, we propose the application of reinforcement learning (RL) to develop a dynamic pricing policy. Our focus is on formulating an optimal pricing strategy in a duopoly setting to maximize revenue, outperforming competitor pricing. Unlike previous studies, we eliminate assumptions about demand estimations, characterizing customer behavior through a utility function incorporating price, product quality, ratings and perishability. We employ the Soft Actor-Critic (SAC) algorithm for its sample efficiency and faster convergence, ensuring consistency. This research presents a novel approach to dynamic pricing in competitive markets, offering valuable insights for revenue maximization strategies.