Coherent beam combining (CBC) 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 [1]. This is relatively complicated and cost 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. Building on our previous experience about the use of reinforcement learning in CBC [2], here we show that it is possible to derive a suitable control policy without any explicit modelling using deep reinforcement learning in a simulated environment.

© 2019 IEEE

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