Multi-agent reinforcement learning framework for autonomous traffic signal control in smart cities - Olusanya, Olamide O.; Owosho, Yetunde; Daniyan, Ilesanmi; Elegbede, Adedayo W.; Sodipo, Queen B.; Adeodu, Adefemi; Phuluwa, Humbulani Simon; Ramasu, Tlotlo K.; Kana-Kana Katumba, Mukondeleli Grace (Frontiers in Mechanical Engineering, 2025)
IntroductionThe increasing urbanization across the world necessitate efficient traffic management especially in the emerging economies. This paper presents an intelligent framework aimed at enhancing traffic signal management within complex road networks through the creation and evaluation of a multi-agent reinforcement learning (MARL) framework.MethodsThe research explored how Reinforcement Learning (RL) algorithms can be employed to optimize the flow of traffic, lessen bottleneck, and enhance overall transportation safety and efficiency. Additionally, the research explored the design and simulation of a typical traffic environment that is, an intersection, defined and implemented a Multi-Agent System (MAS), and developed a Multi-Agent reinforcement learning model for traffic management within a simulated environment this model leverages actor-critics and deep Q Network (DQN) strategies for learning and coordination, and performed the evaluation of the MARL model. Novel approaches for decentralized decision-making and dynamic resource allocation were developed to enable real-time adaptation to changing traffic conditions and emergent situations. Performance evaluation using metrics such as waiting time, queue length, and congestion were carried out in the SUMO simulation platforms (Simulation of Urban Mobility) to evaluate the efficiency of the proposed solution in various traffic scenarios.Results and DiscussionThe outcome of the simulation conducted in this study showed an improvement in queue management and traffic flow by 64.5% and 70.0% respectively with improvement in performance of the proposed model over the episodes. The results show that the RL model policy showed better performance compared to the baseline policy, indicating that the model learned over different episodes. The results also show that the MARL-based approach performs better for decentralized traffic control systems in both scalability and adaptability. The proposed solution supports real-time decision-making, reduces traffic congestion, and improves the efficiency of the urban transportation system.