Multi-Agent Reinforcement Learning for Traffic Control with Dynamic Route Optimization

Abstract

This paper presents a novel approach to traffic control utilizing Multi-Agent Reinforcement Learning (MARL) for dynamic route optimization within a traffic network. Traditional traffic management systems often struggle to adapt effectively to fluctuating traffic conditions and emergent congestion. This research proposes a decentralized system where individual vehicles are treated as intelligent agents, learning optimal routes through interaction and reinforcement learning. The core mechanism leverages the MARL framework to allow vehicles to adapt to real-time traffic data, considering the actions of neighboring vehicles. The system aims to minimize overall travel time and congestion by dynamically adjusting routes based on learned policies. Simulation results demonstrate the potential of this approach to significantly improve traffic flow compared to static routing or centralized control strategies. The key contributions lie in the decentralized, adaptive nature of the system, enabling robust performance in complex and dynamic traffic environments. Mathematical formulations and algorithms are presented to detail the system's operation and performance evaluation.