Reinforcement Learning for Accelerator Beamline Control: a simulation-based approach

• URL: http://arxiv.org/abs/2510.26805v1
• Date: Sat, 18 Oct 2025 11:02:54 GMT
• Title: Reinforcement Learning for Accelerator Beamline Control: a simulation-based approach
• Authors: Anwar Ibrahim, Alexey Petrenko, Maxim Kaledin, Ehab Suleiman, Fedor Ratnikov, Denis Derkach,
• Abstract summary: We introduce RLABC, a Python-based library that reframes beamline optimization as a reinforcement learning (RL) problem.<n> RLABC automates the creation of an RL environment from standard lattice and element input files, enabling sequential tuning of magnets to minimize particle losses.<n>We demonstrate RLABC's efficacy on two beamlines, achieving transmission rates of 94% and 91%, comparable to expert manual optimizations.
• Score: 0.764101887158157
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Particle accelerators play a pivotal role in advancing scientific research, yet optimizing beamline configurations to maximize particle transmission remains a labor-intensive task requiring expert intervention. In this work, we introduce RLABC (Reinforcement Learning for Accelerator Beamline Control), a Python-based library that reframes beamline optimization as a reinforcement learning (RL) problem. Leveraging the Elegant simulation framework, RLABC automates the creation of an RL environment from standard lattice and element input files, enabling sequential tuning of magnets to minimize particle losses. We define a comprehensive state representation capturing beam statistics, actions for adjusting magnet parameters, and a reward function focused on transmission efficiency. Employing the Deep Deterministic Policy Gradient (DDPG) algorithm, we demonstrate RLABC's efficacy on two beamlines, achieving transmission rates of 94% and 91%, comparable to expert manual optimizations. This approach bridges accelerator physics and machine learning, offering a versatile tool for physicists and RL researchers alike to streamline beamline tuning.

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