Abstract

With the rapid development of our society and economy, traffic congestion caused by the increase in vehicles is an important challenge we are facing today. Traffic control, as an integral part of intelligent transport systems (ITS), plays a huge role in traffic control areas. A good signal control method can greatly improve the traffic capacity of vehicles on the road. In this paper, based on the traditional DQN algorithm, we propose a gradient algorithm based on meta-learning to promote the adjustment of the training parameters of the DQN algorithm and add an attention mechanism in the depth neural network to aggregate the effective features. Finally, we adjust the loss function to obtain a more effective DQN algorithm for road signal control. Simulation experiments conducted by SUMO simulation software prove the effectiveness of our method.

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