本文在综合神经网络与强化学习的基础上,对智能交通区域协调控制进行了研究。文章首先介绍了基于神经网络存储和训练的强化学习算法,然后在此基础上进行区域协调模型的建立并给出协调的算法,通过仿真实验对模型的有效性进行验证,结果显示本文建立的模型对于试验安徽省合肥市高新区区域内的交通状况的提高有较好的效果。 Based on the comprehensive neural network and reinforcement learning, this paper studies the coordinated control of intelligent traffic area. Firstly, the article introduces the reinforcement learning algorithm based on neural network storage and training. Then, based on this, the establishment of the regional coordination model and the coordination algorithm are given. The validity of the model is verified through simulation experiments. The results show that the model Hefei City, Anhui Province for testing high-tech zones within the region to improve traffic conditions have a better effect.