Because of its potential to thoroughly change mobility and transport, autonomous systems and self-driving vehicles are attracting much attention from both the research community and industry. Recent work has demonstrated that it is possible to rely on a comprehensive understanding of the immediate environment while following simple high-level directions, to obtain a more scalable approach that can make autonomous driving a ubiquitous technology. However, to date, the majority of the methods concentrates on deterministic control optimisation algorithms to select the right action, while the usage of deep learning and machine learning is entirely dedicated to object detection and recognition. Recently, we have witnessed a remarkable increase in interest in Reinforcement Learning (RL).