A Deep Reinforcement Learning Approach for Route Planning of Autonomous Vehicles^*

Urban autonomous driving has the potential to enhance both safety and efficiency of transportation in environments also in complex traffic conditions. However, new services and approaches are necessary to manage Autonomous Vehicles in the real traffic. This paper introduces a novel approach to optimize routing in the urban settings by Deep Reinforcement Learning (DRL) techniques. A modular DRL architecture is proposed to obtain a route able to minimize the length of the paths, minimize the number of turns during the travel and select the dedicated lanes. The proposed DRL is implemented on a case study where the agents are trained in a simulation environment for the city center of Bari, a town of Southern Italy.