Analytically Guided Reinforcement Learning for Green It and Fluent Traffic

This study investigates various methods for autonomous traffic signal control. We look into different types of control methods, including fixed time, adaptive, analytic, and reinforcement learning approaches. Machine approaches are compared with the “analytic” approach, which is used as “gold standard” performance assessment. find that conventional machine better than analytic but require a lot more computer power. We, therefore, introduce novel hybrid method called “analytically guided learning” or shorter “?-RL”. approach implemented in our “GuidedLight agent” tends to outperform both, classical while largely improving convergence. therefore suited “green IT” solution improves environmental impact two-fold way: by reducing (i) congestion (ii) processing power needed operation light algorithm.