  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.
