用强化学习自动优化粒子加速器光束线，提升传输效率。
Reinforcement Learning for Accelerator Beamline Control: a simulation-based approach
- 将光束线调优转为强化学习问题，自动调节磁铁参数。
- 在两个光束线上分别实现94%和91%的传输率，接近人工专家水平。
- 适合加速器物理与机器学习交叉研究者使用。
粒子加速器在科学研究中至关重要，但优化光束线配置以最大化粒子传输仍需大量人工干预。本文提出RLABC（Reinforcement Learning for Accelerator Beamline Control），一个基于Python的库，将光束线优化建模为强化学习（RL）问题。利用Elegant仿真框架，RLABC可从标准晶格与元件输入文件自动生成RL环境，实现对磁铁参数的顺序调节以最小化粒子损失。我们定义了包含束流统计的状态表示、用于调整磁铁参数的动作空间，以及以传输效率为核心的奖励函数。采用深度确定性策略梯度（DDPG）算法，在两条光束线上分别实现了94%和91%的传输率，与专家手动优化效果相当。该方法融合加速器物理与机器学习，为物理学家和强化学习研究者提供了一个通用工具，助力光束线调优自动化。
原文摘要 · Abstract (English)
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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