With the rapid development of artificial intelligence, reinforcement learning techniques are emerging, and in the field of transportation, the control of traffic signals can be optimized using reinforcement learning algorithm to improve traffic efficiency. In teaching, the reinforcement learning algorithm is more difficult than the traditional timing algorithm to understand, and there is a lack of teaching platform - which can not only give students an in-depth understanding of the algorithm and advantages of reinforcement learning, but also facilitate teachers to evaluate students' mastery. In view of the above problems, this project designs and develops a traffic signal control optimization simulation teaching platform using AI algorithm, which realizes a number of reinforcement learning algorithms, and combines the basic characteristics of teaching, adopts a distributed system structure, which can meet the needs of online teaching and evaluation.

The platform is divided into teacher side and student side, the main functions of the student side are: SUMO road network simulation model selection, action mechanism selection (and input related parameters), reinforcement learning algorithm selection (Q-learning and Sarsa), model input parameters and evaluation parameters (such as fuel consumption, queue number) selection, algorithm operation, statistical output and so on. During the operation, the platform displays the statistical results in real time in the form of dynamic graphs, and after the operation, outputs static charts of evaluation data of different algorithms. The platform has the function of evaluating and scoring according to the weighted average of the optimization of output results. According to the requirements set by teachers, it can score the aspects of pass efficiency and environmental impact, and generate experimental reports as the basis for teaching evaluation. The teacher can view the completion of the experiment and the evaluation results of the experiment, so as to check the students' learning of the reinforcement learning algorithm.