  We present an approach to learn fast and dynamic robot motions without
exceeding limits on the position $\theta$, velocity $\dot{\theta}$,
acceleration $\ddot{\theta}$ and jerk $\dddot{\theta}$ of each robot joint.
Movements are generated by mapping the predictions of a neural network to
safely executable joint accelerations. The neural network is invoked
periodically and trained via reinforcement learning. Our main contribution is
an analytical procedure for calculating safe joint accelerations, which
considers the prediction frequency $f_N$ of the neural network. As a result,
the frequency $f_N$ can be freely chosen and treated as a hyperparameter. We
show that our approach is preferable to penalizing constraint violations as it
provides explicit guarantees and does not distort the desired optimization
target. In addition, the influence of the selected prediction frequency on the
learning performance and on the computing effort is highlighted by various
experiments.
