Autonomous Control of a Particle Accelerator using Deep Reinforcement Learning

TL;DR

This paper presents a deep reinforcement learning approach to control a particle accelerator, achieving better-than-human performance in beam quality and aiming for near-autonomous operation to reduce tuning time.

Contribution

It introduces a novel AI control framework combining deep neural networks with physics simulation for particle accelerators, demonstrating improved control performance.

Findings

Achieved better-than-human beam current and distribution.

Demonstrated the potential for near-autonomous accelerator tuning.

Abstract

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.