A Method to Design Reinforcement Function Based on Fuzzy Rules in QLearning
ZHAO XiaoHua1, LI ZhenLong2, CHEN YangZhou2, RONG Jian1
1.Key Laboratory of Transportation Engineering in Beijing, Beijing University of Technology, Beijing 1000222. School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022
Abstract：Qlearning 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 Qlearning. A method to design the reward function of Qlearning 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 Qlearning based on fuzzy rules is better than that of conventional Qlearning for traffic signal control.
赵晓华，李振龙，陈阳舟，荣建. Q学习中基于模糊规则的强化函数设计方法[J]. 模式识别与人工智能, 2008, 21(2): 254-259.
ZHAO XiaoHua, LI ZhenLong, CHEN YangZhou, RONG Jian. A Method to Design Reinforcement Function Based on Fuzzy Rules in QLearning. , 2008, 21(2): 254-259.
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2008, Vol. 21 
