用强化学习优化加速器控制，减少人工调参时间。
Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach
- 用Python封装Elegant仿真工具，实现强化学习与加速器模拟的无缝对接。
- 作为物理学家的智能助手，显著缩短调参时间并提升束线性能。
- 适合加速器物理、机器学习交叉研究者参考。
优化加速器控制是实验粒子物理中的关键挑战，传统方法依赖专家经验且耗时耗力。本文提出基于仿真的强化学习框架，以 exttt{Elegant}为仿真后端，开发了Python封装工具，简化强化学习算法与加速器模拟之间的交互，实现输入管理、仿真执行和输出分析的自动化。该框架可作为物理学家的智能协作者，提供优化建议，提升束线性能，缩短调参时间，提高运行效率。以一个典型加速器控制问题为案例，验证了该方法在效率与性能上的改进。研究表明，将仿真工具与基于Python的强化学习框架结合，为加速器物理领域提供了强大工具，展示了机器学习在复杂物理系统优化中的潜力。
原文摘要 · Abstract (English)
Optimizing accelerator control is a critical challenge in experimental particle physics, requiring significant manual effort and resource expenditure. Traditional tuning methods are often time-consuming and reliant on expert input, highlighting the need for more efficient approaches. This study aims to create a simulation-based framework integrated with Reinforcement Learning (RL) to address these challenges. Using \texttt{Elegant} as the simulation backend, we developed a Python wrapper that simplifies the interaction between RL algorithms and accelerator simulations, enabling seamless input management, simulation execution, and output analysis. The proposed RL framework acts as a co-pilot for physicists, offering intelligent suggestions to enhance beamline performance, reduce tuning time, and improve operational efficiency. As a proof of concept, we demonstrate the application of our RL approach to an accelerator control problem and highlight the improvements in efficiency and performance achieved through our methodology. We discuss how the integration of simulation tools with a Python-based RL framework provides a powerful resource for the accelerator physics community, showcasing the potential of machine learning in optimizing complex physical systems.
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