Adaptive Traffic Light Controller with Reinforcement Learning for Reducing Traffic Congestion

Reinforcement Learning is a machine learning method that has been adopted in smart traffic lights to reduce traffic congestion effectively. The algorithm is employed as an agent, like a traffic police officer, that can manage traffic jams with the intelligent capability. In this paper, we revisit the basic Q- Learning method applied to adaptive traffic lights by proposing systematic state-action definition, formulating reward policy, and evaluating the performance. The proposed method basically aims to adjust the duration of green lights at each intersection according to the level of congestion at that intersection dynamically, hence, can adapt with traffic condition. Simulation results confirm that the propose method works effectively in managing traffic congestion by maintaining vehicle queue length almost constant at around 40 vehicles queue, offering an improvement from the conventional method that give growth in queue more than 60 vehicles. In the future, we plan to implement the proposed work in an embedded hardware systems to address the real-time performance requirement.