Traffic Light Control with Policy Gradient-Based Reinforcement Learning

With the increase in population and transportation vehicles, increasing transportation times due to traffic density and carbon dioxide emitted by vehicles is one of the most important problems today. In this study, it is aimed to reduce these times and reduce traffic density by controlling traffic lights within the scope of smart cities. By using Policy Gradient-based reinforcement learning, a realistic city traffic sample is created with various vehicle elements such as cars, motorcycles and bicycles, and traffic lights are controlled to provide a smoother traffic with lower transportation times. Thanks to the application, a one-third improvement was achieved in the 180-hour period presented as an example.