  Autonomous navigation has recently gained great interest in the field of
reinforcement learning. However, little attention was given to the time optimal
velocity control problem, i.e. controlling a vehicle such that it travels at
the maximal speed without becoming dynamically unstable (roll-over or sliding).
  Time optimal velocity control can be solved numerically using existing
methods that are based on optimal control and vehicle dynamics. In this paper,
we use deep reinforcement learning to generate the time optimal velocity
control. Furthermore, we use the numerical solution to further improve the
performance of the reinforcement learner. It is shown that the reinforcement
learner outperforms the numerically derived solution, and that the hybrid
approach (combining learning with the numerical solution) speeds up the
training process.
