A survey of Reinforcement and Deep reinforcement learning for Coordination in Intelligent Traffic Light Control

Abstract Intelligent traffic signal control is required for a transportation system to function properly. In contrast to existing traffic signals, where rules are typically developed manually, an intelligent traffic signal control system should dynamically adapt to real-time traffic. The use of reinforcement learning for intelligent traffic signal control is a growing trend, and recent studies have shown promising results. However, none of the current studies have tested actual traffic data yet. This paper presents the primary techniques learning and methods (RL, DL, DRL).The analysis of each technique, the learning of its strengths and limitations, in order to evaluate at which levels they satisfy the requirements of urban traffic. The paper also lines some of the simulators, which perform adaptive traffic.