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

TL;DR

The paper tackles the challenge of optimizing accelerator beamlines, which traditionally requires extensive expert tuning, by reframing beamline control as a reinforcement-learning problem. It introduces RLABC, a Python library that automatically converts Elegant lattice and element inputs into an RL environment, using a 4D continuous action space and the Deep Deterministic Policy Gradient (DDPG) algorithm with Gaussian exploration to adjust magnet settings and maximize transmission. Across two beamlines, the approach achieves transmission rates of $94\%$ and $91\%$, closely matching or exceeding expert manual optimization, demonstrating the method's effectiveness in simulation. This work bridges accelerator physics and ML, offering a flexible, plug-and-play framework that can scale to diverse beamline architectures and potentially extend to real hardware as part of a physicist's copilot tool.

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.

Figures (9)

• Figure 1: RL interaction cycle: At step $t$, the agent reviews state $S_t$ and reward $R_t$, picks action $A_t$, moves to $S_{t+1}$ with $R_{t+1}$, and iterates to refine performance.
• Figure 2: System layout of RLABC: The RL agent interacts with the environment, which communicates with the Elegant Wrapper and simulation engine.
• Figure 4: The architecture of the first beamline: tall triangles represent the quadrupoles, while the shorter ones that bend the beam are the dipoles.
• Figure 5: The beam charge along the first beamline, demonstrating minimal particle loss as the charge remains nearly constant.
• Figure 6: Beam envelope along the x-axis (red) and y-axis (blue) for the first beamline, with gray lines denoting the aperture. The beam remains fully contained within the aperture throughout.