Reinforcement Learning Journal, vol. TBD, 2025, pp. TBD.
Presented at the Reinforcement Learning Conference (RLC), Edmonton, Alberta, Canada, August 5–9, 2025.
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.
Francesco Capuano, Davorin Peceli, and Gabriele Tiboni. "Shaping Laser Pulses with Reinforcement Learning." Reinforcement Learning Journal, vol. TBD, 2025, pp. TBD.
BibTeX:@article{capuano2025shaping,
title={Shaping Laser Pulses with Reinforcement Learning},
author={Capuano, Francesco and Peceli, Davorin and Tiboni, Gabriele},
journal={Reinforcement Learning Journal},
year={2025}
}