Pekiştirmeli öğrenme yöntemi tabanlı trafik ışık yönetim sistemleri

Traffic lights have been around since 19th century, and aims to ease the chaos happening in intersections. It's recorded that, people spend hours in traffic leading degradations in human health and environment. Even though its main purpose is to reduce traffic congestion and decrease the number of accidents, most of the approaches cannot adapt very well to fast changing dynamics and growing demands of the intersections with modern world developments. Fixed-time approaches use predefined settings, and to maximize its success time slots are identified. Although there are successful attempts, they don't answer today's demands of traffic. To overcome this problem, adaptive controllers are developed, and detectors and sensors are added to systems to enable adoption and dynamism. Recently, reinforcement learning has shown its capability to learn the dynamics of complex environments such as urban traffic. Although it was studied in single junction systems, one of the problems was the lack of consistency with how the real world system works. Most of the systems assume the environment is fully observable or actions would be freely executed using simulators. This study aims to merge usefulness of reinforcement learning methods with real world constraints. The experiments conducted have shown that, with queue data obtained from sensors located at the beginning and at the end of the roads and limited action spaces it works very well and A2C is able to learn the dynamics of the environment while converging and stabilizes itself in a respectively short duration.