基于糊糙集的改进Q学习算法

过金超; 刘杰; 崔光照

doi:10.3969/j.issn.2095-476X.2013.03.010

May 2013

Article Contents

GUO Jin-chao, LIU Jie and CUI Guang-zhao. An improved Q-learning algorithm based on rough set[J]. Journal of Light Industry, 2013, 28(3): 42-45. doi: 10.3969/j.issn.2095-476X.2013.03.010

Citation: GUO Jin-chao, LIU Jie and CUI Guang-zhao. An improved Q-learning algorithm based on rough set[J]. Journal of Light Industry, 2013, 28(3): 42-45. doi: 10.3969/j.issn.2095-476X.2013.03.010 shu

An improved Q-learning algorithm based on rough set

College of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China

Received Date: 2013-01-20
Available Online: 2013-05-15

Abstract

Q-learning algorithm has a fundamental flaw,that is,prone to error intervals overlap,and thus overestimation of the correct Q-value.These are likely to lead to low convergence speed and continuous decline in the performance of Q-learning,an improved Q-learning algorithm was proposed,that was rough sets Q-learning algorithm.The algorithm can be able to minimize the overestimation caused by Q-values and improve performance of learning through effectively deal with incomplete information and uncertain knowledge.Navigation experiments based on these two algorithms were conducted,the results showed that rough sets Q-learning algorithm had higher efficiency of learning and stronger ability of obstacle avoidance than Q-learning algorithm.
- Q-learning algorithm,
- rough set,
- robot navigation
References
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沈阳化工大学材料科学与工程学院沈阳 110142

An improved Q-learning algorithm based on rough set

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通讯作者: 陈斌, bchen63@163.com