University of Trieste

Simple scheme of the FERMI FEL seed laser alignment set up.

Feasibility Investigation on Several Reinforcement Learning Techniques to Improve the Performance of the FERMI Free-Electron Laser

N. Bruchon University of Trieste PhD thesis Abstract The research carried out in particle accelerator facilities does not concern only particle and condensed matter physics, although these are the main topics covered in the field. Indeed, since a particle accelerator is composed of many different sub-systems, its proper functioning depends both on each of these parts and their interconnection. It follows that the study, implementation, and improvement of the various sub-systems are fundamental points of investigation too....

Plot of the reward received by the agent versus step number.

Policy gradient methods for free-electron laser and terahertz source optimization and stabilization at the FERMI free-electron laser at Elettra

F. H. O’Shea1, N. Bruchon2, G. Gaio1 1Elettra Sincrotrone Trieste, 2University of Trieste Physical Review Accelerators and Beams Abstract In this article we report on the application of a model-free reinforcement learning method to the optimization of accelerator systems. We simplify a policy gradient algorithm to accelerator control from sophisticated algorithms that have recently been demonstrated to solve complex dynamic problems. After outlining a theoretical basis for the functioning of the algorithm, we explore the small hyperparameter space to develop intuition about said parameters using a simple number-guess environment....

A schematic overview of theAE-DYNAapproach used in this paper.

Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL

S. Hirlaender1, N. Bruchon2 1University of Salzburg, 2University of Trieste arXiv Abstract Reinforcement learning holds tremendous promise in accelerator controls. The primary goal of this paper is to show how this approach can be utilised on an operational level on accelerator physics problems. Despite the success of model-free reinforcement learning in several domains, sample-efficiency still is a bottle-neck, which might be encompassed by model-based methods. We compare well-suited purely model-based to model-free reinforcement learning applied to the intensity optimisation on the FERMI FEL system....

The RL paradigm as applied to particle accelerator control, showing the example of trajectory correction.

Sample-efficient reinforcement learning for CERN accelerator control

V. Kain1, S. Hirlander1, B. Goddard1, F. M. Velotti1, G. Z. Della Porta1, N. Bruchon2, G. Valentino3 1CERN, 2University of Trieste, 3University of Malta Physical Review Accelerators and Beams Abstract Numerical optimization algorithms are already established tools to increase and stabilize the performance of particle accelerators. These algorithms have many advantages, are available out of the box, and can be adapted to a wide range of optimization problems in accelerator operation....

Basic Reinforcement Learning Techniques to Control the Intensity of a Seeded Free-Electron Laser

N. Bruchon1, G. Fenu1, G. Gaio2, M. Lonza2, F. H. O’Shea2, F. A. Pellegrino1, E. Salvato1 1University of Trieste, 2Elettra Sincrotrone Trieste Electronics Abstract Optimal tuning of particle accelerators is a challenging task. Many different approaches have been proposed in the past to solve two main problems—attainment of an optimal working point and performance recovery after machine drifts. The most classical model-free techniques (e.g., Gradient Ascent or Extremum Seeking algorithms) have some intrinsic limitations....

Simple scheme of the EOS laser alignment set up.

Toward the Application of Reinforcement Learning to the Intensity Control of a Seeded Free-Electron Laser

N. Bruchon, G. Fenu, G. Gaio, M. Lonza, F. A. Pellegrino, E. Salvato University of Trieste 23rd International Conference on Mechatronics Technology Abstract The optimization of particle accelerators is a challenging task, and many different approaches have been proposed in years, to obtain an optimal tuning of the plant and to keep it optimally tuned despite drifts or disturbances. Indeed, the classical model-free approaches (such as Gradient Ascent or Extremum Seeking algorithms) have intrinsic limitations....

Scheme of the FERMI FEL seed laser alignmentset up

Free-electron Laser Optimization with Reinforcement Learning

N. Bruchon1, G. Gaio2, G. Fenu1, M. Lonza2, F. A. Pellegrino1, E. Salvato1 1University of Trieste, 2Elettra Sincrotrone Trieste 17th International Conference on Accelerator and Large Experimental Physics Control Systems Abstract Reinforcement Learning (RL) is one of the most promis-ing techniques in Machine Learning because of its modestcomputational requirements with respect to other algorithms.RL uses an agent that takes actions within its environmentto maximize a reward related to the goal it is designed toachieve....