Speakers
Description
Abstract:
Optimizing control systems in particle accelerators presents significant challenges, often requiring extensive manual effort and expert knowledge. Traditional tuning methods are time-consuming and may struggle to navigate the complexity of modern beamline architectures. To address these challenges, we introduce a simulation-based framework that leverages Reinforcement Learning (RL) to enhance the control and optimization of beam transport systems. Built on top of the Elegant simulation engine, our Python-based platform automates the generation of simulations and transforms accelerator tuning tasks into RL environments with minimal user intervention. The framework features a modified Soft Actor-Critic agent enhanced with curriculum learning, enabling robust performance across a variety of beamline configurations. Designed with accessibility and flexibility in mind, the system can be deployed by non-experts and adapted to optimize virtually any beamline. Early results demonstrate successful application across multiple simulated beamlines, validating the approach and offering promising potential for broader adoption. We continue to refine the framework toward a general-purpose solution—one that can serve both as an intelligent co-pilot for physicists and a testbed for RL researchers developing new algorithms. This work highlights the growing synergy between AI and accelerator physics, and the critical role of computational innovation in advancing experimental capabilities.
Significance
This presentation introduces a novel RL-based framework for accelerator control that automates the transformation of beamline optimization tasks into simulation-backed RL environments using Elegant. Unlike prior work, our system requires minimal user input, supports curriculum learning, and is designed for generality—enabling optimization across diverse beamlines. It has already demonstrated success on multiple setups and continues to evolve toward a robust co-pilot for physicists and a versatile testbed for RL research. These results represent a meaningful step beyond prior status reports, combining practical impact with methodological innovation.
References
https://arxiv.org/abs/2503.09665