  Machine learning, especially deep learning, is dramatically changing the
methods associated with optical thin-film inverse design. The vast majority of
this research has focused on the parameter optimization (layer thickness, and
structure size) of optical thin-films. A challenging problem that arises is an
automated material search. In this work, we propose a new end-to-end algorithm
for optical thin-film inverse design. This method combines the ability of
unsupervised learning, reinforcement learning(RL) and includes a genetic
algorithm to design an optical thin-film without any human intervention.
Furthermore, with several concrete examples, we have shown how one can use this
technique to optimize the spectra of a multi-layer solar absorber device.
