Autonomous Control of a Particle Accelerator using Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2010.08141v2
• Date: Sun, 20 Dec 2020 00:42:39 GMT
• Title: Autonomous Control of a Particle Accelerator using Deep Reinforcement Learning
• Authors: Xiaoying Pang, Sunil Thulasidasan, Larry Rybarcyk
• Abstract summary: We describe an approach to learning optimal control policies for a large, linear particle accelerator. The framework consists of an AI controller that uses deep neural nets for state and action-space representation. Initial results indicate that we can achieve better-than-human level performance in terms of particle beam current and distribution.
• Score: 2.062593640149623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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