计算机应用研究2011，Vol.28Issue(4)：1266-1268,1271,4.DOI:10.3969/j.issn.1001-3695.2011.04.018
一种新的基于蚁群优化的模糊强化学习算法
Novel fuzzy reinforcement learning incorporated with ant colony optimization
谢光强 1陈学松2
作者信息
- 1. 广东工业大学自动化学院,广州,510006
- 2. 广东工业大学计算机学院,广州,510006
- 折叠
摘要
Abstract
Fuzzy Sarsa learning (FSL) is one of fuzzy reinforcement learning algorithms based on Sarsa architecture.FSL approximates the action value function and is an on-policy method.In each fuzzy rules, actions are selected according to the proposed modified Softmax formula.Because it was difficult for FSL to balance exploration vs.exploitation, offered an ant colony optimization FSL(ACO-FSL) by integrating the proposed ant colony optimization and the fuzzy balancer into FSL, and proved the weight vector of ACO-FSL with stationary action selection policy converged to a unique value.Simulation results show that ACO-FSL well manages balance, and outperforms FSL in terms of learning speed and action quality.关键词
强化学习/模糊Sarsa学习/蚁群优化Key words
reinforcement learning/ fuzzy Sarsa learning/ ant colony optimization分类
信息技术与安全科学引用本文复制引用
谢光强,陈学松..一种新的基于蚁群优化的模糊强化学习算法[J].计算机应用研究,2011,28(4):1266-1268,1271,4.
