Abstract: This paper is concerned with dynamic pricing problems of seasonal goods based on multi-Agent. The Q-learning algorithm and the Wolf-PHC (Win or Learn Fast, Policy Hill-Climbing) algorithm were proposed to learn the dynamic pricing model of seasonal goods which the two providers did not exchange information with each other. Finally, the paper obtained the simulation results of DF (Derivative Following) method, the Q-learning pricing algorithm and the Wolf-PHC pricing algorithm, and the compared results show that the Wolf-PHC pricing algorithm has a more effective optimization.

Key words: seasonal goods, dynamic pricing, Q-learning algorithm, Wolf-PHC algorithm

摘要： 研究两个提供商销售季节性商品时的最优定价策略问题。在性能势理论的基础上，针对季节性商品的特殊属性，建立两个提供商之间没有信息交互情况下的季节性商品的动态定价模型，并引入了Q学习算法和Wolf-PHC算法。通过仿真实验对DF方法定价，Q学习算法定价和Wolf-PHC算法定价进行比较，得到Wolf-PHC算法定价的优化效果更明显，适应性更强。

关键词: 季节性商品, 动态定价, Q学习算法, Wolf-PHC算法