  This paper proposes an adaptable path tracking control system based on
Reinforcement Learning (RL) for autonomous cars. A four-parameter controller
shapes the behavior of the vehicle to navigate on lane changes and roundabouts.
The tuning of the tracker uses an educated Q-Learning algorithm to minimize the
lateral and steering trajectory errors. The CARLA simulation environment was
used both for training and testing. The results show the vehicle is able to
adapt its behavior to the different types of reference trajectories, navigating
safely with low tracking errors. The use of a ROS bridge between the CARLA and
the tracker results (i) in a realistic system, and (ii) simplifies the
replacement of the CARLA by a real vehicle. An argument on the dependability of
the overall architecture based on stability results of non-smooth systems is
presented at the end of the paper.
