Abstract
This review paper provides a comprehensive analysis of recent advancements in leveraging reinforcement learning (RL) and deep reinforcement learning (DRL) techniques for optimizing traffic signal control systems within urban environments. Traditional traffic signal management methods often fail to address the inefficiencies resulting in congestion, delays, environmental impacts, and driver dissatisfaction. In contrast, RL methodologies empower traffic signals to dynamically learn and refine signal timing strategies by iteratively interacting with real-world traffic conditions. DRL builds upon RL by integrating deep neural networks to better capture and respond to intricate traffic patterns. The paper investigates the potential of RL and DRL approaches in addressing diverse urban traffic challenges across peak and non-peak periods, as well as in areas with irregular traffic distributions. It delves into the strengths, weaknesses, underlying principles, and advanced architectures of current RL/DRL methodologies proposed for this domain. Furthermore, it evaluates practical applications and simulation studies, showcasing performance enhancements over traditional signal control methods. In conclusion, the review consolidates key findings and outlines future research directions for implementing RL/DRL systems in real-world scenarios to effectively alleviate urban traffic congestion.