  We describe the offline machine learning (ML) development for an effort to
precisely regulate the Gradient Magnet Power Supply (GMPS) at the Fermilab
Booster accelerator complex via a Field-Programmable Gate Array (FPGA). As part
of this effort, we created a digital twin of the Booster-GMPS control system by
training a Long Short-Term Memory (LSTM) to capture its full dynamics. We
outline the path we took to carefully validate our digital twin before
deploying it as a reinforcement learning (RL) environment. Additionally, we
demonstrate the use of a Deep Q-Network (DQN) policy model with the capability
to regulate the GMPS against realistic time-varying perturbations.
