  The design and implementation of low-profile antennas has been analyzed in
past decades from different perspectives while the purpose is to have a small
size in the device, and an adequate electromagnetic behavior. This work pursues
a methodology to identify small antennas and consequently presents some
similarities. Meanwhile, curved surfaces are considered for a certain variety
of antennas with reduced size. The so-called deep reinforcement learning
technique is used as an assistance against morphological variations that are
specifically taken into account in this work. The objective is to identify
antennas that can be efficiently mounted on the surface of metal tubes such as
those frequently present in public infrastructure (e.g. traffic lights and
luminaries). The motivation is to reduce the visual impact and optimize the
radiation pattern of the antenna. It is analyzed that if changes in variables
such as the radius of curvature, or the electromagnetic properties of the
materials appear, an automatic identification of the underlying characteristics
of the problem (by means of machine learning techniques) can readjust the
design efficiently. The results obtained in this work are analyzed based on
variables that are typically used to characterize antennas, such as their
impedance and radiation pattern.
