  Traffic signal control is a significant part of the construction of
intelligent transportation. An efficient traffic signal control strategy can
reduce traffic congestion, improve urban road traffic efficiency and facilitate
people's lives. Existing reinforcement learning approaches for traffic signal
control mainly focus on learning through a separate neural network. Such an
independent neural network may fall into the local optimum of the training
results. Worse more, the collected data can only be sampled once, so the data
utilization rate is low. Therefore, we propose the Random Ensemble Double DQN
Light (RELight) model. It can dynamically learn traffic signal control
strategies through reinforcement learning and combine random ensemble learning
to avoid falling into the local optimum to reach the optimal strategy.
Moreover, we introduce the Update-To-Data (UTD) ratio to control the number of
data reuses to improve the problem of low data utilization. In addition, we
have conducted sufficient experiments on synthetic data and real-world data to
prove that our proposed method can achieve better traffic signal control
effects than the existing optimal methods.
