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. Prior work has already shown the impressive approximation power of SIREN networks, most notably for image and video reconstruction. We argue that since learning optimal control policies via behavioural cloning is essentially a regression task over a highly discontinuous function, akin to image reconstruction, it is unsurprising that the SIREN also excels in the context of G&CNETs.