Road Traffic: Deep Q-learning Agent Control Traffic lights in the intersection.

Abstract

In recent decades, road traffic has increased in line with the attractiveness of cities. As a result, motorists are increasingly faced with traffic jams, which have many consequences. To find solutions to this problem, it is necessary to understand the origin of congestion. this is why this document proposes a strategy for managing intersections by controlling traffic signals at an intersection aimed at reducing the rate of congestion where we present a deep reinforcement learning (Deep RL) model that is a development and implementation work of the deep Q-learning algorithm that manages an agent in a simulated traffic environment using the SUMO (Simulation of Urban Mobility) traffic Road Simulator.