基于分层强化学习的智慧小镇客流管理系统设计

With the rapid development of artificial intelligence technology, more and more automatic decision-making systems are relying on the control of intelligent algorithms. As an intelligent algorithm that dynamically adapts to the environment, reinforcement learning is widely applied in fields such as recommendation algorithms, robotic control, and traffic management. In order to optimize the overall profits and improve the shopping experience, many shopping scenarios require control and management of customer flow. In this paper, we propose an intelligent customer flow management method based on reinforcement learning and digital simulation. By training reinforcement learning agents in a digital simulation environment, we provide an automatic and intelligent customer flow management policy in the smart towns, which can better serve the customers in smart towns, leading to a better shopping experience.