Q学习中基于模糊规则的强化函数设计方法

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Abstract Qlearning is a reinforcement learning method to solve Markovian decision problems with incomplete information. The design of reward function is an important factor that affects the learning results of Qlearning. A method to design the reward function of Qlearning based on fuzzy rules is introduced to improve the performance of reinforcement learning, and the method is applied to traffic signal optimal control. According to different traffic condition, the switching time and switching sequence of phase can be adapted. The performance of the system is evaluated by Paramics microcosmic traffic simulation software. And the results show that the learning effect of Qlearning based on fuzzy rules is better than that of conventional Qlearning for traffic signal control.

Key words： QLearning Reinforcement Function Fuzzy Rules Traffic Signal Control Paramics Microcosmic Traffic Simulation Software

Received: 07 June 2006

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	ZHAO XiaoHua
	LI ZhenLong
	CHEN YangZhou
	RONG Jian

Cite this article:

ZHAO XiaoHua,LI ZhenLong,CHEN YangZhou等. A Method to Design Reinforcement Function Based on Fuzzy Rules in QLearning[J]. , 2008, 21(2): 254-259.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2008/V21/I2/254

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