14 citations · 20 across the 6 of their papers we have counts for
6 papers
Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task
Reuf Kozlica, Stefan Wegenkittl, Simon Hirländer
This paper presents a comparison between two well-known deep Reinforcement Learning (RL) algorithms: Deep Q-Learning (DQN) and Proximal Policy Optimization (PPO) in a simulated pro…
A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications
Reuf Kozlica, Georg Schäfer, Simon Hirländer +1
This application paper explores the potential of using reinforcement learning (RL) to address the demands of Industry 4.0, including shorter time-to-market, mass customization, and…
Automatic setup of 18 MeV electron beamline using machine learning
Francesco Maria Velotti, Brennan Goddard, Verena Kain +3
To improve the performance-critical stability and brightness of the electron bunch at injection into the proton-driven plasma wakefield at the AWAKE CERN experiment, automation app…
Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL
Simon Hirlaender, Niky Bruchon
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 acce…
Online Multi-Objective Particle Accelerator Optimization of the AWAKE Electron Beam Line for Simultaneous Emittance and Orbit Control
Alexander Scheinker, Simon Hirlaende, Francesco Maria Velotti +4
Multi-objective optimization is important for particle accelerators where various competing objectives must be satisfied routinely such as, for example, transverse emittance vs bun…
Gamma Factory at CERN -- novel research tools made of light
W. Placzek, A. Abramov, S. E. Alden +59
We discuss the possibility of creating novel research tools by producing and storing highly relativistic beams of highly ionised atoms in the CERN accelerator complex, and by excit…