  This paper explores the use of reinforcement learning (RL) models for
autonomous racing. In contrast to passenger cars, where safety is the top
priority, a racing car aims to minimize the lap-time. We frame the problem as a
reinforcement learning task with a multidimensional input consisting of the
vehicle telemetry, and a continuous action space. To find out which RL methods
better solve the problem and whether the obtained models generalize to driving
on unknown tracks, we put 10 variants of deep deterministic policy gradient
(DDPG) to race in two experiments: i)~studying how RL methods learn to drive a
racing car and ii)~studying how the learning scenario influences the capability
of the models to generalize. Our studies show that models trained with RL are
not only able to drive faster than the baseline open source handcrafted bots
but also generalize to unknown tracks.
