The lap time optimisation is the process to calculate the best trajectory to achieve the minimum lap time, while considering the boundaries due to the vehicle dynamics. Traditional approaches have leaned on physics-based control models, which, although effective in offline settings, fall short when applied to online racing scenarios. These models not only burden computational resources but also lack the adaptability required to respond promptly to dynamic variations in vehicle behaviour and track conditions. The work of this thesis consists of a reinforcement learning algorithm capable to act as a path re-planner with access to previously stored information coming from previous manoeuvres.