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In recent years, several studies have been conducted on the dynamic control of traffic signal durations using deep reinforcement learning with the aim of reducing traffic congestion. The unique advantages of independent control of traffic signals include reduction in the cost of information transmission and stable control without being affected by the failure of other traffic signals. However, conventional deep reinforcement learning methods such as Deep Q-Network may degrade the learning performance in a multi-agent environment where there are multiple traffic signals in the environment. Therefore, we propose a traffic light control system based on the dual targeting algorithm, which incorporates reinforcement of successful experiences in multi-agent environments, with the aim of realizing a better traffic light control system. The experimental results in a multi-agent environment using a traffic flow simulator based on simulation of urban mobility (SUMO) show that the proposed traffic light control system reduces the waiting time at traffic lights by 33% compared to a conventional traffic light control system using deep reinforcement learning. In the future works, we aim to apply this research to traffic light control systems in real environments.

Traffic Signal Control System Using Deep Reinforcement Learning with Emphasis on Reinforcing Successful Experiences