Training method of traffic signal control model and traffic signal control method

Abstract

The invention discloses a traffic signal control model training method and a traffic signal control method, and the training method comprises the steps: obtaining the current traffic state data of a target intersection from a preset simulation environment, inputting the current traffic state data into an initial traffic signal control model, and obtaining a phase motion value space; selecting a pre-execution phase action by using the preset agent, and generating an experience sample corresponding to the pre-execution phase action based on the reward value; for each preset agent, determining a corresponding target experience sample from the shared experience pool by using the preset agent according to the corresponding exploration rate; and training the initial traffic signal control model by using the plurality of determined target experience samples to obtain a trained target traffic signal control model. According to the method, the traffic signal control model is trained by using the multiple agents in the training process, so that the training time is shortened, and the algorithm execution efficiency is improved.

Description

technical field [0001] The present application relates to the technical field of deep learning, in particular, to a traffic signal control model training method and a traffic signal control method. Background technique [0002] With the continuous development of my country's economy and society, the number of urban vehicles has increased year by year, and the traffic conditions in urban streets have deteriorated year by year. In order to solve this problem, it is necessary to improve the traffic signal control method to improve the traffic efficiency of the road. At present, in many cities in China, most traffic signal control methods use timing control. However, timing control cannot make corresponding timing changes with traffic conditions, so it is difficult to meet increasingly complex traffic conditions. [0003] With the continuous development of artificial intelligence technology, it is a trend to apply deep reinforcement learning methods to the traffic field to sol...

AI Technical Summary

Problems solved by technology

In the process of using deep reinforcement learning to solve traffic signal control problems, a large amount of training data is often required to train the model, and it takes a long time to directly train using the traditional structure

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