Multi-Agent Reinforcement Learning for Dynamic Urban Traffic Signal Control

This paper presents a multi-agent reinforcement learning system that dynamically optimizes urban traffic signal control in real-time. Experimental results demonstrate significant reductions in traffic congestion and travel times, paving the way for smarter city infrastructure.