Deep reinforcement learning for coherent beam combining applications

Coherent beam combining is a method to scale the peak and average power levels of laser systems beyond the limit of a single emitter system. This is achieved by stabilizing the relative optical phase of multiple lasers and combining them. We investigated the use of reinforcement learning (RL) and neural networks (NN) in this domain. Starting from a randomly initialized neural network, the system converged to a phase stabilization policy, which was comparable to a software implemented proportional-integral-derivative (PID) controller. Furthermore, we demonstrate the capability of neural networks to predict relative phase noise, which is one potential advantage of this method.