Towards Piston Fine Tuning of Segmented Mirrors through Reinforcement Learning
Unlike supervised machine learning methods, reinforcement learning allows an entity to learn how to deploy a task from experience rather than labeled data. This approach has been used in this paper to correct piston misalignment between segments in a segmented mirror telescope. It was proven in simu...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-05-01
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Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/10/9/3207 |
Summary: | Unlike supervised machine learning methods, reinforcement learning allows an entity to learn how to deploy a task from experience rather than labeled data. This approach has been used in this paper to correct piston misalignment between segments in a segmented mirror telescope. It was proven in simulations that the algorithm converges to a point where it learns how to move the piston actuators in order to maximize the Strehl ratio of the wavefront at the intersection. |
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ISSN: | 2076-3417 |