Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach

2025-03-12

AI Summary

Manual beam tuning in experimental particle physics is time-consuming and heavily reliant on expert knowledge. Method: This paper proposes the first RL-Simulation closed-loop co-architectural framework tailored for accelerator physics, deeply integrating reinforcement learning (specifically PPO and DQN) into the Elegant particle tracking simulation pipeline to establish an end-to-end intelligent beam-tuning assistance system. A custom Python interface enables real-time simulation–control closed-loop operation, augmented by physics-informed state representation and reward modeling to minimize human intervention. Contribution/Results: Evaluated on representative beamline tasks, the system reduces beam tuning time by 60% and improves beam quality stability by 40%, thereby demonstrating the feasibility and engineering efficacy of RL for real-time control of complex physical systems.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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 exttt{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.

Problem

• Optimize accelerator control using Reinforcement Learning.
• Reduce manual effort and resource expenditure in tuning.
• Enhance beamline performance and operational efficiency.

Innovation

• Reinforcement Learning optimizes accelerator control
• Python wrapper integrates Elegant simulation with RL
• RL framework reduces tuning time, enhances performance