Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment
This addresses inventory management challenges for retailers or supply chain managers, but it is incremental as it builds on existing DRL methods with a two-timescale approach.
The paper tackles dynamic pricing and replenishment under inconsistent decision frequencies by proposing a dual-agent deep reinforcement learning algorithm, achieving convergence to local optimum and validating effectiveness in numerical scenarios.
We study the dynamic pricing and replenishment problems under inconsistent decision frequencies. Different from the traditional demand assumption, the discreteness of demand and the parameter within the Poisson distribution as a function of price introduce complexity into analyzing the problem property. We demonstrate the concavity of the single-period profit function with respect to product price and inventory within their respective domains. The demand model is enhanced by integrating a decision tree-based machine learning approach, trained on comprehensive market data. Employing a two-timescale stochastic approximation scheme, we address the discrepancies in decision frequencies between pricing and replenishment, ensuring convergence to local optimum. We further refine our methodology by incorporating deep reinforcement learning (DRL) techniques and propose a fast-slow dual-agent DRL algorithm. In this approach, two agents handle pricing and inventory and are updated on different scales. Numerical results from both single and multiple products scenarios validate the effectiveness of our methods.