Reinforcement learning-based control law on PAPYRUS: simulations using different atmospheric conditions

Raissa Camelo; Jalo Nousiainen; Cedric Taïssir Heritier; Morgan Gray; Benoit Neichel

doi:10.1117/12.3018887

27 August 2024 Reinforcement learning-based control law on PAPYRUS: simulations using different atmospheric conditions

Raissa Camelo, Jalo Nousiainen, Cedric Taïssir Heritier, Morgan Gray, Benoit Neichel

Proceedings Volume 13097, Adaptive Optics Systems IX; 1309710 (2024) https://doi.org/10.1117/12.3018887
Event: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

Abstract

Predictive control laws for Adaptive Optics (AO) using Artificial Intelligence has been recently explored as an alternative to the classic methods, such as the integrator law. Reinforcement Learning excels in predictive control tasks by enabling systems to learn optimal control strategies through continuous interaction with their environment, adapting to dynamic conditions and achieving effective decision-making in real-time. In our previous work, a Model-based Reinforcement Learning (MBRL) method called Policy Optimization for Adaptive Optics (PO4AO) was used in conjunction with the Object-Oriented Python Adaptive Optics (OOPAO) to simulate the Provence Adaptive Optics Pyramid Run System (PAPYRUS) optical bench. PO4AO demonstrated high adaptability to turbulence and rapid convergence, achieving optimal corrections after just 500 frames of interaction, outperforming a simulated integrator in different atmospheric conditions. Building upon this, our current study explored PO4AO’s capability to adapt to sudden atmospheric changes by worsening turbulence conditions during evaluation, notably the wind speed and the seeing. In the result’s section, we compare PO4AO’s performance in terms of Strehl Ratio (SR) to the integrator. Further description of the experiments are present in the paper.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Raissa Camelo, Jalo Nousiainen, Cedric Taïssir Heritier, Morgan Gray, and Benoit Neichel "Reinforcement learning-based control law on PAPYRUS: simulations using different atmospheric conditions", Proc. SPIE 13097, Adaptive Optics Systems IX, 1309710 (27 August 2024); https://doi.org/10.1117/12.3018887

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available