TempoRL: laser pulse temporal shape optimization with deep reinforcement learning

F. Capuano; D. Peceli; G. Tiboni; R. Camoriano; B. Rus

doi:10.1117/12.2669267

9 June 2023 TempoRL: laser pulse temporal shape optimization with deep reinforcement learning

F. Capuano, D. Peceli, G. Tiboni, R. Camoriano, B. Rus

Proceedings Volume 12577, High-power, High-energy Lasers and Ultrafast Optical Technologies; 125770C (2023) https://doi.org/10.1117/12.2669267
Event: SPIE Optics + Optoelectronics, 2023, Prague, Czech Republic

Conference Poster

Abstract

High Power Laser’s (HPL) optimal performance is essential for the success of a wide variety of experimental tasks related to light-matter interactions. Traditionally, HPL parameters are optimized in an automated fashion relying on black-box numerical methods. However, these can be demanding in terms of computational resources and usually disregard transient and complex dynamics. Model-free Deep Reinforcement Learning (DRL) offers a promising alternative framework for optimizing HPL performance since it allows to tune the control parameters as a function of system states subject to nonlinear temporal dynamics without requiring an explicit dynamics model of those. Furthermore, DRL aims to find an optimal control policy rather than a static parameter configuration, particularly suitable for dynamic processes involving sequential decision making. This is particularly relevant as laser systems are typically characterized by dynamic rather than static traits. Hence the need for a strategy to choose the control applied based on the current context instead of one single optimal control configuration. This paper investigates the potential of DRL in improving the efficiency and safety of HPL control systems. We apply this technique to optimize the temporal profile of laser pulses in the L1 pump laser hosted at the ELI Beamlines facility. We show how to adapt DRL to the setting of spectral phase control by solely tuning dispersion coefficients of the spectral phase and reaching pulses similar to transform limited with full-width at half-maximum (FWHM) of ∼1.6 ps. The code base of this work, alongside a live demo of the results obtained, is available at github.com/fracapuano/TempoRL.

Citation Download Citation

F. Capuano, D. Peceli, G. Tiboni, R. Camoriano, and B. Rus "TempoRL: laser pulse temporal shape optimization with deep reinforcement learning", Proc. SPIE 12577, High-power, High-energy Lasers and Ultrafast Optical Technologies, 125770C (9 June 2023); https://doi.org/10.1117/12.2669267

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