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Title	Multi-Junction Traffic Light Control System With Reinforcement Learning In Sunway Smart City
ID_Doc	38227
Authors	Lam H.C.; Wong R.T.K.; Jasser M.B.; Chua H.N.; Issa B.
Year	2024
Published	2024 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2024 - Proceedings
DOI	http://dx.doi.org/10.1109/I2CACIS61270.2024.10649841
Abstract	This paper presents a reinforcement learning approach to address urban traffic congestion with traffic light control optimization. The proposed system leverages Q-learning and Markov Decision Processes (MDPs) to develop an adaptive control framework capable of adjusting traffic signal timings dynamically. A neural network model is employed to estimate Q-values, representing cumulative rewards for various control actions. The training process involves interactions between the agent and a simulated traffic environment using the Simulation of Urban Mobility (SUMO) tool. Experimental results showcase the efficacy of the RL-based approach in mitigating congestion and improving traffic flow. This work focuses on the case study of a network of junctions that contribute to the main traffic flow in the township of Bandar Sunway. © 2024 IEEE.
Author Keywords	adaptive traffic light system; Markov decision process; q-learning; reinforcement learning; traffic light control

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