Published May 20, 2022 | Version v1
Presentation Open

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

  • 1. Barcelona Supercomputing Center, Universitat Politècnica de Catalunya
  • 2. School of Computing, Australian National University
  • 3. Barcelona Supercomputing Center
  • 4. Universitat Politècnica de Catalunya
  • 5. LESIA , Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université de Paris, Australian National University


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.



Files (1.3 MB)

Name Size Download all
1.3 MB Preview Download

Additional details


Rising STARS – RISE International Network for Solutions Technologies and Applications of Real-time Systems 873120
European Commission
AMPERE – A Model-driven development framework for highly Parallel and EneRgy-Efficient computation supporting multi-criteria optimisation 871669
European Commission