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Description
This work highlights the experimental framework employed to implement and validate Deep Deterministic Policy Gradient (DDPG) for controlling a Fabry-Perot (FP) optical cavity, a key component in interferometric gravitational-wave detectors. An initial focus is placed on the real-world setup characterisation, where high finesse values and mirror velocities introduce significant non-linearities.
DDPG, a model-free, off-policy algorithm that efficiently handles continuous action spaces, is used to address these challenges. It integrates actor-critic networks with experience replay and slow-updating target networks, to achieve stable learning. In addition, we apply input and output normalization which mitigates issues arising from diverse physical units and variable input scales, facilitating robust policy updates and portability without exhaustive manual tuning.
To transition from simulation to the physical system, the FP cavity is first accurately modelled in a high-fidelity simulator. Strategies, such as accounting for delays and noise sources, are incorporated to minimize the reality gap to address sim-to-real transfer. The trained DDPG agent is then deployed on the hardware, demonstrating how deterministic policy gradients can adapt to real-time feedback, latency, and environmental uncertainties. This integration of simulation, DDPG-based control, and experimental measurement represents a significant step toward reliable and autonomous optical cavity locking, paving the way for advanced control in gravitational-wave detection and other high-precision photonic applications.
