Optimizing Dynamic Pricing through Reinforcement Learning: Techniques, Case Studies, and Implementation Challenges

Nishant Gadde, Avaneesh Mohapatra, Shreyan Dey, Ishan Das, Vedant Bhatia, Gagan Reddy

Abstract: Dynamic pricing is one of the important tools in the realm of modern business, as it allows firms to automatically change prices based on either demand, competition, or inventory. Traditional pricing models are based on static rules, which cannot keep pace with the rapidly changing market conditions. Reinforcement learning grants businesses an exploratory license to the development of adaptive pricing systems, which learn from past data how to dynamically adjust their pricing policy in order to optimize long-term profits. Several reinforcement learning techniques are discussed in this work as being applied to dynamic pricing: Q-learning, Deep Q-Networks, and Proximal Policy Optimization. Various case studies from industries such as e-commerce, ride sharing, and airlines will then be used to demonstrate the effectiveness of reinforcement learning-based pricing models. However, there are many challenges during its implementation, like large dataset requirements, overfitting risks, ethical considerations of fairness, and transparency. Further, embedding customer feedback inside the model and embedding the RL framework within other machine-learning techniques will leverage both accuracy and interpretability.

Keywords: dynamic pricing, reinforcement learning, adaptive pricing, e-commerce, pricing optimization