A Non Linear Control Method with Reinforcement Learning for Adaptive Optics with Pyramid Sensors

Extreme Adaptive Optics (AO) systems are designed to provide high resolution and high contrast observing capabilities on the largest ground-based telescopes through exquisite phase reconstruction accuracy. In that context, the pyramid wavefront sensor (P-WFS) has shown promise to deliver the means to provide such accuracy due to its high sensitivity. However, traditional methods cannot leverage the highly non-linear P-WFS measurements to their full potential. We present a predictive control method based on Reinforcement Learning (RL) for AO control with a P-WFS. The proposed approach is data-driven, has no assumptions about the system's evolution, and is non-linear due to the usage of neural networks. First, we discuss the challenges of using an RL control method with a P-WFS and propose solutions. Then, we show that our method outperforms an optimized integrator controller. Finally, we discuss its possible path for an actual implementation.