Optimizing Dynamic Pricing with Reinforcement Learning

Author(s): Shenggang Li

Originally published on Towards AI.

Utilizing DDPG and SHAP for Pricing Strategies in Retail
Photo by Brooke Lark on Unsplash

Retail pricing strategies are important for optimizing sales and profits. Effective pricing influences consumer behavior and maximizes revenue by considering demand, market conditions, and competition. For example, retailers can strategically adjust prices and apply discounts to boost sales and increase profitability.

This paper explores a reinforcement learning approach using the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize pricing strategies. By dynamically adjusting prices and discounts, we can improve pricing decisions. Additionally, SHAP (Shapley Additive Explanations) values provide insights into the impact of price, discount, and sales on the model’s decisions. This combined approach enhances the traditional pricing model by incorporating real-time analysis and explainable AI techniques.

Pricing strategies in retail can be mathematically modeled to optimize sales and profits. The sales function can be written as:

This implies that sales depend on various factors, primarily price and discount. Typically, an increase in price results in decreased sales, and vice versa. The goal is to find an optimal price that maximizes sales or profits. For example, if the sales function follows a quadratic form:

where a and b are constants, optimization techniques such as quadratic or linear programming can be used to find the best price.

However, traditional optimization methods… Read the full blog for free on Medium.

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Published via Towards AI