Multi-Agent based dynamic pricing algorithm for seasonal goods
LU Hui1,2
1. Department of Electronic Information, Anhui Finance and Trade Vocational College, Hefei Anhui 230601， China 2. School of Computer Science and Technology, Hefei University of Technology, Hefei Anhui 230009, China
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.
2011, Vol. 31 
