  We study learning based controllers as a replacement for model predictive
controllers (MPC) for the control of autonomous vehicles. We concentrate for
the experiments on the simple yet representative bicycle model. We compare
training by supervised learning and by reinforcement learning. We also discuss
the neural net architectures so as to obtain small nets with the best
performances. This work aims at producing controllers that can both be embedded
on real-time platforms and amenable to verification by formal methods
techniques.
