SELECTIVE GENERALIZATION OF CMAC FOR Q-LEARNING AND ITS APPLICATION TO LAYOUT PLANNING OF CHEMICAL PLANTS

摘要：

This paper proposes a modified design method of CMAC integrated in a reinforcement learning system to solve the allocation problem for the chemical plant.In the proposed method, the generalization of CMAC is selectively settled for each measure according to characteristics of measures.This feature is efficacious to improve the learning performance of the system.In application examples, by using the proposed method, the elements of the chemical plant are placed by choosing the position having the best evaluation that is obtained by adequate learning iteration.Then this procedure gives an allocation plan with minimized risk and maximized efficiency.In addition, rotated and/or shifted plants that have the same layout can be identically recognized, so that the learning performance can be improved.

关键词： CMAC Generalization Plant allocation problem Q-learning

作者: YOICHI HIRASHIMA

作者单位: Osaka Institute of Technology, 1-79-1 Kita-Yama, Hirakata,Osaka, 573-0196, Japan

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 2071-2076

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）