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Adaptive optics control using model-based reinforcement learning.

Jalo Nousiainen, Chang Rajani, Markus Kasper

    Optics Express
    |May 14, 2021
    PubMed
    Summary

    Reinforcement learning (RL) offers a novel control method for adaptive optics (AO) in astronomy. This approach effectively manages temporal delays and calibration errors, enhancing AO system performance.

    Area of Science:

    • Astronomy
    • Control Systems Engineering
    • Artificial Intelligence

    Background:

    • Adaptive optics (AO) systems are crucial for high-resolution astronomical observations.
    • Traditional AO control methods face challenges with temporal delays and calibration inaccuracies.
    • Reinforcement learning (RL) presents a potential solution to these limitations.

    Purpose of the Study:

    • To investigate the application of model-based reinforcement learning (MBRL) for controlling AO systems.
    • To evaluate the effectiveness of MBRL in addressing temporal delays and calibration errors in AO.
    • To demonstrate MBRL's capability for continuous learning and adaptation in AO control.

    Main Methods:

    • Formulating the AO control loop as a model-based reinforcement learning (MBRL) problem.

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  • Implementing and simulating the MBRL approach on a Shack-Hartmann Sensor (SHS) based AO system.
  • Utilizing a simulated AO system with 24 resolution elements.

    • Main Results:

    • MBRL-controlled AO successfully predicted the temporal evolution of atmospheric turbulence.
    • The MBRL system demonstrated an ability to adjust to mis-registration errors between the deformable mirror and SHS.
    • Continuous learning was observed on timescales of seconds, enabling adaptation to changing conditions.

    Conclusions:

    • Model-based reinforcement learning is a promising approach for advanced AO system control in astronomy.
    • MBRL can autonomously handle complex AO challenges like temporal delays and calibration issues.
    • The adaptive nature of MBRL allows for robust performance under dynamic environmental conditions.