Tyler M. McCracken

Autonomous alignment of a Mach-Zehnder interferometer via deep reinforcement learning: a foundation for resilient space-based and ground-based optical control

This paper presents a Deep Reinforcement Learning (DRL) framework for the autonomous alignment of a Mach-Zehnder Interferometer (MZI). Utilizing a Proximal Policy Optimization (PPO) algorithm and a Convolutional Neural Network (CNN) policy, the agent manages two-degree-of-freedom mirror adjustments to achieve a null-fr...