# Self-optimizing adaptive optics control with Reinforcement Learning for high-contrast imaging

arXiv: https://arxiv.org/abs/2108.11332 · HTML: https://mlsanity.com/paper/2108.11332/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 65 references that are in the map.
- First posted on arXiv 24 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Memory-based control with recurrent neural networks](https://mlsanity.com/paper/1512.04455/) (2015)

## Built on by (most cited first, 4 of 4)

- [Tempestas ex machina: A review of machine learning methods for wavefront control](https://mlsanity.com/paper/2309.00730/) (2023)
- [Laboratory Experiments of Model-based Reinforcement Learning for Adaptive Optics Control](https://mlsanity.com/paper/2401.00242/) (2023)
- [Model-free reinforcement learning with noisy actions for automated experimental control in optics](https://mlsanity.com/paper/2405.15421/) (2024)
- [Hybrid deep learning-based phase diversity method for wavefront reconstruction](https://mlsanity.com/paper/2606.25855/) (2026)

## Abstract (arXiv)

Current and future high-contrast imaging instruments require extreme adaptive optics (XAO) systems to reach contrasts necessary to directly image exoplanets. Telescope vibrations and the temporal error induced by the latency of the control loop limit the performance of these systems. One way to reduce these effects is to use predictive control. We describe how model-free Reinforcement Learning can be used to optimize a Recurrent Neural Network controller for closed-loop predictive control. First, we verify our proposed approach for tip-tilt control in simulations and a lab setup. The results show that this algorithm can effectively learn to mitigate vibrations and reduce the residuals for power-law input turbulence as compared to an optimal gain integrator. We also show that the controller can learn to minimize random vibrations without requiring online updating of the control law. Next, we show in simulations that our algorithm can also be applied to the control of a high-order deformable mirror. We demonstrate that our controller can provide two orders of magnitude improvement in contrast at small separations under stationary turbulence. Furthermore, we show more than an order of magnitude improvement in contrast for different wind velocities and directions without requiring online updating of the control law.