  Manual optimization of traffic light cycles is a complex and time-consuming
task, necessitating the development of automated solutions. In this paper, we
propose the application of reinforcement learning to optimize traffic light
cycles in real-time. We present a case study using the Simulation Urban
Mobility simulator to train a Deep Q-Network algorithm. The experimental
results showed 44.16% decrease in the average number of Emergency stops,
showing the potential of our approach to reduce traffic congestion and improve
traffic flow. Furthermore, we discuss avenues for future research and
enhancements to the reinforcement learning model.
