Shaping Laser Pulses with Reinforcement Learning

Francesco Capuano, Davorin Peceli, Gabriele Tiboni; 6:2652−2666, 2025.

Presented at the Reinforcement Learning Conference (RLC), Edmonton, Alberta, Canada, August 5–9, 2025.

Abstract

High Power Laser (HPL) systems operate in the femtosecond regime---the shortest timescale achievable in experimental physics. HPL systems are instrumental in high-energy physics, leveraging ultra-short impulse durations to yield extremely high intensities, which are essential for both practical applications and theoretical advancements in light-matter interactions. Traditionally, the parameters regulating HPL optical performance are tuned manually by human experts, or optimized by using black-box methods that can be computationally demanding. Critically, black box methods rely on stationarity assumptions overlooking complex dynamics in high-energy physics and day-to-day changes in real-world experimental settings, and thus need to be often restarted. Deep Reinforcement Learning (DRL) offers a promising alternative by enabling sequential decision making in non-static settings. This work investigates the safe application of DRL to HPL systems, and extends the current research by (1) learning a control policy directly from images and (2) addressing the need for generalization across diverse dynamics. We evaluate our method across various configurations and observe that DRL effectively enables cross-domain adaptability, coping with dynamics' fluctuations while achieving 90\% of the target intensity in test environments.

[abs][pdf]

BibTeX

@article{capuano2025shaping,
    title={Shaping Laser Pulses with Reinforcement Learning},
    author={Capuano, Francesco and Peceli, Davorin and Tiboni, Gabriele},
    journal={Reinforcement Learning Journal},
    volume={6},
    pages={2652--2666},
    year={2025}
}