摘要
Q 学习算法是求解信息不完全马尔可夫决策问题的一种强化学习方法.Q 学习中强化信号的设计是影响学习效果的重要因素.本文提出一种基于模糊规则的 Q 学习强化信号的设计方法,提高强化学习的性能.并将该方法应用于单交叉口信号灯最优控制中,根据交通流的变化自适应调整交叉口信号灯的相位切换时间和相位次序.通过 Paramics 微观交通仿真软件验证,说明在解决交通控制问题中,使用基于模糊规则的 Q 学习的学习效果优于传统 Q 学习.
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.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2008年第2期254-259,共6页
Pattern Recognition and Artificial Intelligence
