摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新数据在一份新的报告中呈现。根据NewsRx记者从中国大连发回的新闻报道，研究表明：“自由电子激光(FEL)设备的激光优化是一项耗时且具有挑战性的任务。机器学习算法的实现为自由电子激光优化提供了一种快速、适应性强的方法，而不是由经验丰富的操作人员手工操作。”本研究的资金来源包括国家重点研发项目、国家自然科学基金(NSFC)、中国科学院科学仪器开发项目、DICP、中国博士后科学基金、中国科学院专项研究助理资助项目、大连大荔市先进光源预研项目。我们的新闻编辑引用了中国科学院的一篇研究文章：“最近，我们在大连相干光源(DCLS)真空紫外探测器上进行了这种实验。标准遗传算法和神经网络遗传算法、深度决策策略梯度算法和软演员批评强化学习算法四种算法，分别是标准遗传算法和基于神经网络的遗传算法、深度决策策略梯度算法和软演员批评强化学习算法。”这些算法在增强FEL激光方面表现出了显著的效果，特别是强化学习算法，它只需400次迭代就能收敛。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Dalian, People's Repub lic of China, by NewsRx correspondents, research stated, "The lasing optimizatio n of Free -Electron Laser (FEL) facilities is a time-consuming and challenging t ask. Instead of operating manually by experienced operators, implementation of m achine learning algorithms offers a rapid and adaptable approach for FEL lasing optimization." Funders for this research include National Key R & D Program of Ch ina, National Natural Science Foundation of China (NSFC), Scientific Instrument Developing Project of Chinese Academy of Science, DICP, China Postdoctoral Scien ce Foundation, Specific Research Assistant Funding Program from Chinese Academy of Sciences, Pre-study Project of Dalian Advanced Light Source from city of Dali an. Our news editors obtained a quote from the research from the Chinese Academy of Sciences, "Recently, such an experiment has been conducted at the vacuum ultravi olet FEL facility - Dalian Coherent Light Source (DCLS). Four algorithms, namely the standard and the neural network -based genetic algorithms, the deep determi nistic policy gradient and the soft actor critic reinforcement learning algorith ms, have been employed to enhance the FEL intensity by optimizing the electron b eam trajectory. These algorithms have shown notable efficacy in enhancing the FE L lasing, especially the reinforcement learning ones which achieved convergence within only approximately 400 iterations."
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