  Inspired by the versatility of sinusoidal representation networks (SIRENs),
we present a modified Guidance & Control Networks (G&CNETs) variant using
periodic activation functions in the hidden layers. We demonstrate that the
resulting G&CNETs train faster and achieve a lower overall training error on
three different control scenarios on which G&CNETs have been tested previously.
A preliminary analysis is presented in an attempt to explain the superior
performance of the SIREN architecture for the particular types of tasks that
G&CNETs excel on.
