English(EN) RL-ABC: Reinforcement Learning for Accelerator Beamline Control

新的强化学习框架实现粒子加速器束线控制自动化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员开发了RL-ABC，一个开源Python框架，使用强化学习来优化粒子加速器束线。该框架将标准的束线配置转换为强化学习环境，并与Elegant等模拟代码集成。它自动预处理晶格文件，从束统计数据构建状态表示，并提供用于传输优化的可配置奖励函数。使用RL-ABC训练的深度确定性策略梯度（Deep Deterministic Policy Gradient）代理在测试束线上实现了70.3%的粒子传输率，与现有方法相当，证明了该框架的有效性和效率。 AI

影响该框架通过自动化复杂的控制问题，有望加速粒子物理领域的研究和优化。

排序理由该集群包含一篇学术论文，详细介绍了特定科学领域的新方法和开源框架。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Anwar Ibrahim, Fedor Ratnikov, Maxim Kaledin, Alexey Petrenko, Denis Derkach · 2026-10-02 04:00

RL-ABC：用于加速器束线控制的强化学习

arXiv:2604.19146v2 Announce Type: replace Abstract: Particle accelerator beamline optimization is a high-dimensional control problem traditionally requiring significant expert intervention. We present RLABC (Reinforcement Learning for Accelerator Beamline Control), an open-source…

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RL-ABC：用于加速器束线控制的强化学习

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