We present a case for the use of Reinforcement Learning (RL) for the design of physics instrument as an alternative to gradient-based instrument-optimization methods. It's applicability is demonstrated using two empirical studies. One is longitudinal segmentation of calorimeters and the second is both transverse segmentation as well longitudinal placement of trackers in a spectrometer. Based on these experiments, we propose an alternative approach that offers unique advantages over differentiable programming and surrogate-based differentiable design optimization methods. First, Reinforcement Learning (RL) algorithms possess inherent exploratory capabilities, which help mitigate the risk of convergence to local optima. Second, this approach eliminates the necessity of constraining the design to a predefined detector model with fixed parameters. Instead, it allows for the flexible placement of a variable number of detector components and facilitates discrete decision-making. We then discuss the road map of how this idea can be extended into designing very complex instruments. The presented study sets the stage for a novel framework in physics instrument design, offering a scalable and efficient framework that can be pivotal for future projects such as the Future Circular Collider (FCC), where most optimized detectors are essential for exploring physics at unprecedented energy scales.

翻译：我们提出了一种使用强化学习（RL）进行物理仪器设计的方法，作为基于梯度的仪器优化方法的替代方案。其适用性通过两项实证研究得到验证。第一项是量热器的纵向分段研究，第二项是谱仪中追踪器的横向分段以及纵向放置研究。基于这些实验，我们提出了一种替代方法，相较于可微分编程和基于代理的可微分设计优化方法，该方法具有独特的优势。首先，强化学习算法具备固有的探索能力，有助于降低收敛至局部最优解的风险。其次，该方法无需将设计约束在具有固定参数的预定义探测器模型内。相反，它允许灵活放置可变数量的探测器组件，并支持离散决策。随后，我们讨论了如何将这一思路扩展至设计非常复杂仪器的路线图。本研究为物理仪器设计建立了一个新颖的框架，提供了一个可扩展且高效的框架，这对于未来项目（如未来环形对撞机）至关重要，因为高度优化的探测器对于在前所未有的能量尺度上探索物理学现象至关重要。