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.

该研究提出一种名为 RElight 的动态学习交通信号控制策略的模型，并结合随机集成学习方法以避免陷入局部最优结果。研究还引入了 UTD 比率以控制数据重用数量，以提高数据利用率并在合成数据和实际数据上进行了实验以证明该方法优于现有最优方法。