Shaping Laser Pulses with Reinforcement Learning

• URL: http://arxiv.org/abs/2503.00499v1
• Date: Sat, 01 Mar 2025 14:00:41 GMT
• Title: Shaping Laser Pulses with Reinforcement Learning
• Authors: Francesco Capuano, Davorin Peceli, Gabriele Tiboni,
• Abstract summary: High Power Laser (HPL) systems operate in the femtosecond regime.<n>Deep Reinforcement Learning (DRL) offers a promising alternative by enabling sequential decision making in non-static settings.
• Score: 1.0923877073891446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High Power Laser (HPL) systems operate in the femtosecond regime--one of the shortest timescales 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.

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