关键词: 季节性商品, 动态定价, Q学习算法, Wolf-PHC算法

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