  We describe a method for precisely regulating the gradient magnet power
supply at the Fermilab Booster accelerator complex using a neural network
trained via reinforcement learning. We demonstrate preliminary results by
training a surrogate machine-learning model on real accelerator data to emulate
the Booster environment, and using this surrogate model in turn to train the
neural network for its regulation task. We additionally show how the neural
networks to be deployed for control purposes may be compiled to execute on
field-programmable gate arrays. This capability is important for operational
stability in complicated environments such as an accelerator facility.
