# Autonomous Control of a Particle Accelerator using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2010.08141 · HTML: https://mlsanity.com/paper/2010.08141/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language](https://mlsanity.com/paper/2405.08888/) (2024)
- [Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning](https://mlsanity.com/paper/2306.03739/) (2023)
- [Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments](https://mlsanity.com/paper/2405.15508/) (2024)
- [Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach](https://mlsanity.com/paper/2503.09665/) (2025)
- [Outlook Towards Deployable Continual Learning for Particle Accelerators](https://mlsanity.com/paper/2504.03793/) (2025)
- [Adaptive Model-Based Reinforcement Learning for Orbit Feedback Control in NSLS-II Storage Ring](https://mlsanity.com/paper/2601.03486/) (2026)
- [Toward a Fully Autonomous, AI-Native Particle Accelerator](https://mlsanity.com/paper/2602.17536/) (2026)

## Abstract (arXiv)

We describe an approach to learning optimal control policies for a large, linear particle accelerator using deep reinforcement learning coupled with a high-fidelity physics engine. The framework consists of an AI controller that uses deep neural nets for state and action-space representation and learns optimal policies using reward signals that are provided by the physics simulator. For this work, we only focus on controlling a small section of the entire accelerator. Nevertheless, initial results indicate that we can achieve better-than-human level performance in terms of particle beam current and distribution. The ultimate goal of this line of work is to substantially reduce the tuning time for such facilities by orders of magnitude, and achieve near-autonomous control.