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为了减少车辆通过路口的延误，采用云模型建立控制策略，运用Q-学习改进控制模型的参数。路口信号控制智能体通过感知系统获得车辆到达信息，根据信号控制规则集和车辆到达信息采取符合控制策略的控制行为改变当前信号状态。信号控制的关键规则采用二维正态半云描述，利用二维前件云发生器生成针对不同交通状态的控制策略。云模型中的主要参数通过Q学习算法进行优化，以总停车延迟作为目标函数经过迭代产生针对不同交通量的云模型最优控制方案。最后，使用仿真软件对传统控制方式和基于云模型的控制方式进行比较，仿真结果表明，基于云模型信号控制方法的控制效果优于定时控制和感应控制。

Self-learning Approach of Traffic Signal Control Based on Cloud Model

In order to reduce the delay of cars passing through intersections, control strategies are set up by cloud model and some parameters of the control model are improved by Q-learning method. The signal control agent of intersections gets the information of vehicles arriving through its perception system. The current signal state is changed according to the set of signal control rules and the information of vehicles arriving. The key rule of the set of signal control rules is described by two-dimensional half normal clouds. The signal control strategies facing different traffic states are generated by two-dimensional forward cloud generators. The main parameters of cloud model are optimized by Q-learning algorithm. Regarding the total vehicle stop delay as objective function, the optimal control schemes based on the cloud model under different traffic volume are generated by iteration. Finally, the signal control method based on the cloud model is compared with the traditional approaches through simulation. The result of the simulation indicates that the effect of the control method based on the cloud model is better than the fixed and actuated control approaches.