Multi Agent reinforcement learning of Real – Time traffic optimization

Multi-Agent Reinforcement Learning (MARL) has emerged as a promising approach for addressing complex, dynamic optimization problems in intelligent transportation systems. This paper presents a MARL-based framework for real-time traffic signal control aimed at reducing congestion, minimizing vehicle waiting time, and improving overall traffic flow efficiency in urban environments. In the proposed system, multiple agents, each representing an individual traffic intersection, learn optimal control policies through interaction with a simulated traffic environment. The agents employ reinforcement learning techniques to adaptively adjust signal timings based on real-time traffic conditions, enabling decentralized decision-making and scalability. A reward mechanism is designed considering metrics such as queue length, delay, and throughput to guide learning toward system-wide optimization. Experimental results obtained using simulation tools demonstrate that the MARL-based approach significantly outperforms traditional fixed-time and actuated signal control methods in terms of average travel time and congestion reduction. The study highlights the potential of distributed intelligent control systems in developing efficient and adaptive smart city traffic management solutions. Furthermore, the proposed framework emphasizes robustness and adaptability in highly dynamic traffic environments where demand patterns fluctuate unpredictably. By leveraging decentralized coordination among agents, the system reduces dependency on centralized control and improves fault tolerance. Communication strategies between neighboring agents are incorporated to enhance cooperative decision-making and avoid local optima that may negatively impact global traffic flow. The learning model is continuously updated using real-time state observations, enabling faster convergence and improved responsiveness to sudden traffic changes such as accidents or peak-hour surges.