Reinforcement Learning for Accelerator Beamline Control: a simulation-based approach
This work addresses the problem of reducing expert intervention in accelerator beamline control for physicists and RL researchers, offering a versatile tool, though it is incremental as it applies an existing RL method to a new domain.
The paper tackles the labor-intensive optimization of particle accelerator beamline configurations by introducing RLABC, a reinforcement learning library that automates tuning to maximize particle transmission, achieving rates of 94% and 91% on two beamlines, comparable to expert manual methods.
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.