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

Anwar Ibrahim, Denis Derkach, Alexey Petrenko, Fedor Ratnikov, Maxim Kaledin

TL;DR

This work tackles the challenge of manually tuning accelerator beamlines by introducing a simulation-based reinforcement learning framework that couples a Python wrapper with the Elegant beamline simulator. An episode-driven RL setup uses a multi-term reward to prioritize maximized particle transmission and minimized losses, with a SAC-based agent (initially from stable-baselines3 and later refined in a custom implementation) learning control policies for magnet currents along a seven-quadrupole beamline. The approach is validated on two emittance scenarios, achieving $100$% transmission for nominal emittance ($2000$) and $67.6$% for high emittance ($32000$), demonstrating robustness to beam quality variations. The framework reduces manual tuning effort, improves operational efficiency, and provides a foundation for future multi-objective optimization and real-time accelerator control.

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 \texttt{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.

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

TL;DR

This work tackles the challenge of manually tuning accelerator beamlines by introducing a simulation-based reinforcement learning framework that couples a Python wrapper with the Elegant beamline simulator. An episode-driven RL setup uses a multi-term reward to prioritize maximized particle transmission and minimized losses, with a SAC-based agent (initially from stable-baselines3 and later refined in a custom implementation) learning control policies for magnet currents along a seven-quadrupole beamline. The approach is validated on two emittance scenarios, achieving % transmission for nominal emittance () and % for high emittance (), demonstrating robustness to beam quality variations. The framework reduces manual tuning effort, improves operational efficiency, and provides a foundation for future multi-objective optimization and real-time accelerator control.

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 \texttt{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.

Paper Structure

This paper contains 11 sections, 1 equation, 2 figures.

Table of Contents

  1. METHODOLOGY
  2. Episodes
  3. Environment
  4. State
  5. Reward
  6. Action
  7. Agent
  8. NUMERICAL EXPERIMENTS
  9. Experiment 1: Nominal Beam Emittance
  10. Experiment 2: Increased Beam Emittance
  11. CONCLUSION

Figures (2)

  • Figure 1: Schematic of the beamline, showing quadrupole magnets (red) and dipole magnets (blue). The beam starts at the source and terminates at the target. Sequential decision-making is employed to optimize magnet settings at each step.
  • Figure 2: Beam profile at the target for Experiment 1. Emittance = 2000. The top panel shows a stable particle count along the beamline, indicating 100% particle delivery. The middle panel shows the magnet current profiles, while the bottom panel illustrates the particle distribution along $x$ and $y$ axes within the aperture.