动态模糊Q学习算法及嵌入式平台的实时实现<sup>*</sup>

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Abstract A new dynamic fuzzy Qlearning (DFQL) method is presented in this paper which is capable of tuning fuzzy inference systems (FIS) online. In DFQL system, the generation of continuous actions depends upon a discrete number of actions of every fuzzy rule and the vector of firing strengths of fuzzy rule. In order to explore the set of possible actions and acquire experiences through the reinforcement signals, the actions are selected using an explorationexploitation strategy based on the expended greedy algorithm. A function Q that gives the action quality with eligibility trace and meta learning rule is used here to speed up learning. εcompleteness of fuzzy rules criterion and temporaldifference (TD) error criterion are considered for rule generation. The DFQL approach has been applied to a realtime control caterpillar robot for the wall following task. Experimental results and comparative studies with the fuzzy Qlearning and continuousaction Qlearning in the wallfollowing task of mobile robots demonstrate that the proposed DFQL method is superior.

Key words： Fuzzy Control OnLine SelfOrganizing QLearning Embedded System RealTime Control

Received: 27 January 2005

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	Articles by authors
	LU YongKui
	XU Min
	LI YongXin
	DU HuaSheng
	WU YueHua
	YANG Jie

Cite this article:

LU YongKui,XU Min,LI YongXin等. Dynamic Fuzzy QLearning and Its RealTime Application in Embedded System[J]. , 2006, 19(4): 439-444.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2006/V19/I4/439

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