A Multi-Agent Reinforcement Learning Framework for Adaptive Traffic Signal Control in Real Urban Networks: Simulation, Performance Analysis, and Statistical Evaluation
Urban traffic congestion continues to pose a significant challenge for modern cities, calling for intelligent traffic signal control (TSC) strategies able to coordinate multiple interconnected intersections under dynamic traffic conditions. To address this challenge, we propose a Hybrid Q-learning–SARSA–Monte Carlo Mul...