| 基于RBF函数状态离散化的激励学习 |
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| 引用本文： | 田建军,唐中勇. 基于RBF函数状态离散化的激励学习[J]. 太原师范学院学报(自然科学版), 2006, 5(3): 50-53 |
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| 作者姓名： | 田建军 唐中勇 |
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| 作者单位： | 1. 湖南公安高等专科学校,计算机系,湖南,长沙,410138
2. 长沙理工大学,计算机通讯工程学院,湖南,长沙,410076 |
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| 摘 要： | 介绍了激励学习和两类学习算法：Q学习和SARSA学习，提出一类基于RBF函数的特征状态离散化方法，并对该方法进行了初步的实验比较．
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| 关 键 词： | 激励学习 特征状态 状态离散化 RBF函数 |
| 文章编号： | 1672-2027（2006）03-0050-04 |
| 收稿时间： | 2006-03-09 |
| 修稿时间： | 2006-03-09 |
The State Discretization Based on RBF Function for the Reinforcement Learning |
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| Tian Jianjun,Tang Zhongyong. The State Discretization Based on RBF Function for the Reinforcement Learning[J]. Journal of Taiyuan Normal University:Natural Science Edition, 2006, 5(3): 50-53 |
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| Authors: | Tian Jianjun Tang Zhongyong |
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| Affiliation: | 1. Department of Computer Science,Hunan Public Security College,Changsha 410138; 2. Department of Computer and Communication,Changsha University of Science and Technology,Changsha 410076,China |
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| Abstract: | Reinforcement Learning and two classes of learning algorithms is introduced. A class of the state discretization based on RBF function for the Reinforcement Learning is proposed and preliminary empirical results are presented to compare the performance of the new method. |
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| Keywords: | reinforcement learning feature state sate discretization RBF function |
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