A New Deep Reinforcement Learning-Based Adaptive Traffic Light Control Approach for Isolated Intersection

In this work, we focus on optimizing traffic signal control at an isolated intersection and subsequently alleviate the traffic flow. We propose a new Deep Reinforcement Learning-based approach. Thus, the traffic network controller in an isolated intersection is modelled as an intelligent agent that perceives the discrete state encoding of traffic information as the network inputs. Our contribution resides to use a Double Deep Q-Network (DDQN). This argues that the idea of having a simplified state and reward formula facilitates the training of the agent by simplifying the convergence of the latter. It dynamically select the phases improving the traffic quality. The experimental results shows that the proposed approach is competitive in terms of Average Waiting Time, Average Queue Length, Average Fuel Consumption and Average Emission CO2 at intersection when compared to some baseline methods.