Optimizing Road Traffic Through Reinforcement Learning
In the article, modern approaches to the development of Intelligent Transportation Systems (ITS) aimed at optimizing urban traffic are analyzed. Special attention is paid to model-free reinforcement learning algorithms (Q-Learning and Deep Q-Learning) used for controlling traffic lights in dynamic road traffic conditions. Simulation results in the SUMO environment have proven that implementing such algorithms significantly reduces intersection queues and increases the capacity of the transportation network.