  From higher computational efficiency to enabling the discovery of novel and
complex structures, deep learning has emerged as a powerful framework for the
design and optimization of nanophotonic circuits and components. However, both
data-driven and exploration-based machine learning strategies have limitations
in their effectiveness for nanophotonic inverse design. Supervised machine
learning approaches require large quantities of training data to produce
high-performance models and have difficulty generalizing beyond training data
given the complexity of the design space. Unsupervised and reinforcement
learning-based approaches on the other hand can have very lengthy training or
optimization times associated with them. Here we demonstrate a hybrid
supervised learning and reinforcement learning approach to the inverse design
of nanophotonic structures and show this approach can reduce training data
dependence, improve the generalizability of model predictions, and shorten
exploratory training times by orders of magnitude. The presented strategy thus
addresses a number of contemporary deep learning-based challenges, while
opening the door for new design methodologies that leverage multiple classes of
machine learning algorithms to produce more effective and practical solutions
for photonic design.
