| 再励学习在交通信号控制中的应用 |
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| 引用本文： | 李丹 孙云兰 何希勤. 再励学习在交通信号控制中的应用[J]. 鞍山科技大学学报, 2003, 26(5): 329-332,336 |
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| 作者姓名： | 李丹 孙云兰 何希勤 |
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| 作者单位： | [1]鞍山科技大学应用数学研究所，辽宁鞍山114044 [2]北京联合大学自动化学院．北京l00l01 |
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| 摘 要： | 再励学习是一种利用评价信息(而不是网络实际输出与期望输出之差)采改善行为的神经模糊算法，采用“奖”“罚”信号训练控制器．用再励学习的目的建立一个可调的模糊交通信号控制器，它能在不同交通情况下修改隶属函数参数，以达到较好的控制效果．其评价指标是车辆延误．仿真结果表明，再励学习在交通量稳定的交叉口信号控制中表现良好．
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| 关 键 词： | 模糊集 神经网络 交通信号控制 再励学习 车辆延误 仿真 |
| 文章编号： | 1672-4410(2003)05-0329-05 |
Reinforcement learning applied in traffic signal control |
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| LI Dan ,SHN Yun_lan ,HE Xi_qin. Reinforcement learning applied in traffic signal control[J]. Journal of Anshan University of Science and Technology, 2003, 26(5): 329-332,336 |
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| Authors: | LI Dan SHN Yun_lan HE Xi_qin |
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| Affiliation: | LI Dan 1,SHN Yun_lan 2,HE Xi_qin 1 |
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| Abstract: | Reinforcement learning is a neurofuzzy algorithm which improves action by using criterion rather than the difference of the network output and the desired output and trains controller by using credit and punishment signals. The objective of the reinforcement learning is to create an adjustable fuzzy traffic signal controller that can modify its parameters in different traffic situations, and then reach a better control result. The criterion is the delay of vehicles. Simulation shows that the learning algorithm is found successfully at stable traffic volumes. |
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| Keywords: | Fuzzy sets Neural networks Traffic signal control Reinforcement learning |
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