Gespeichert in:
| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2509.14884 |
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Inhaltsangabe:
- 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.