Autonomous Control of a Particle Accelerator using Deep Reinforcement Learning
X. Pang1, S. Thulasidasan2, L. Rybarcyk2 1Apple, 2Los Alamos National Laboratory Machine Learning for Engineering Modeling, Simulation, and Design Workshop at Neural Information Processing Systems 2020 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 networks for state and action-space representation and learns optimal policies using reward signals that are provided by the physics simulator....