Retail Inventory Management Based on Deep Reinforcement Learning
In the rapidly evolving digital business environment,to maintain competitiveness amid fierce market competition,brick-and-mortar retailers urgently need to adopt effective methods to optimize their inventory management.This study is grounded in deep reinforcement learning theory and applies the Actor-Critic algorithm to provide scientific guidance for optimizing inventory management for brick-and-mortar retailers.Specifically,the study constructed a simulated training environment using real historical sales data,and employed deep neural networks to learn and optimize inventory management strategies for multiple product categories under limited capacity constraints.This study holds significant theoretical and practical value,offering new insights and methods for brick-and-mortar retailers to achieve efficient inventory management in a competitive market environment.
deep reinforcement learningAdvantage Actor-Criticretailinventory management
万方数据
维普
