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Applying reinforcement learning to optical cavity locking tasks: considerations on actor-critic architectures and real-time hardware implementation
by Mateusz Bawaj, Andrea Svizzeretto
Submission summary
| Authors (as registered SciPost users): | Mateusz Bawaj |
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| Preprint Link: | https://arxiv.org/abs/2509.14884v1 (pdf) |
| Date submitted: | Sept. 19, 2025, 8:54 a.m. |
| Submitted by: | Mateusz Bawaj |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 2nd European AI for Fundamental Physics Conference (EuCAIFCon2025) |
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| Academic field: | Physics |
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| Approach: | Computational |
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Abstract
This proceedings contains our considerations made during and after fruitful discussions held at EuCAIFCon 2025. We explore the use of deep reinforcement learning for autonomous locking of Fabry-Perot optical cavities in non-linear regimes, with relevance to gravitational-wave detectors. A custom Gymnasium environment with a time-domain simulator enabled training of agents such as deep deterministic policy gradient, achieving reliable lock acquisition for both low- and high-finesse cavities, including Virgo-like parameters. We also discuss possible improvements with Twin Delayed DDPG, Soft Actor Critic and meta-reinforcement learning, as well as strategies for low-latency execution and off-line policy updates to address hardware limitations. These studies lay the groundwork for future deployment of reinforcement learning-based control in real optical setups.