  In this paper, we introduce the design approach of integrated photonic
devices by employing reinforcement learning known as attractor selection. Here,
we combined three-dimensional finite-difference time-domain method with
attractor selection algorithm, which is based on artificial neural networks, to
achieve ultra-compact and highly efficient photonic devices with low crosstalk
such as wavelength demultiplexers and a polarization splitter. The presented
devices consist of silicon-on-insulator materials, which are compatible with
complementary metal-oxide-semiconductor technology, and their structural
dimensions enable the possible fabrication process in the future. The numerical
results are presented for the near-infrared wavelengths at around 1550 nm, and
the performance of designed photonic devices with footprint of 3x3 um2 are
compared with the previously reported structures. Consequently, the
reinforcement learning is successfully applied to design smaller and superior
integrated photonic devices where the use of presented approach can be further
expanded to different applications.
