Deep reinforcement learning for tiled aperture beam combining in a simulated environment

Abstract

Coherent beam combining is a method for combining multiple emitters into one high power beam by means of relative phase stabilization. Usually, modulation or interferometric techniques are used to generate an error signal. This is relatively complicated and expensive. Especially in the case of tiled aperture combining the beam profile is usually monitored anyway. This beam profile should contain most of the information necessary for the stabilization as well but is usually not used because it is difficult to explicitly derive the correct actions from just the far-field image. Here we show that it is possible to derive a suitable control policy without any explicit modeling using deep reinforcement learning in a simulated environment.