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We introduce a novel Proximal Policy Optimization (PPO) algorithm aimed ataddressing the challenge of maintaining a uniform proton beam intensitydelivery in the Muon to Electron Conversion Experiment (Mu2e) at Fermi NationalAccelerator Laboratory (Fermilab). Our primary objective is to regulate thespill process to ensure a consistent intensity profile, with the ultimate goalof creating an automated controller capable of providing real-time feedback andcalibration of the Spill Regulation System (SRS) parameters on a millisecondtimescale. We treat the Mu2e accelerator system as a Markov Decision Processsuitable for Reinforcement Learning (RL), utilizing PPO to reduce bias andenhance training stability. A key innovation in our approach is the integrationof a neuralized Proportional-Integral-Derivative (PID) controller into thepolicy function, resulting in a significant improvement in the Spill DutyFactor (SDF) by 13.6%, surpassing the performance of the current PID controllerbaseline by an additional 1.6%. This paper presents the preliminary offlineresults based on a differentiable simulator of the Mu2e accelerator. It pavesthe groundwork for real-time implementations and applications, representing acrucial step towards automated proton beam intensity control for the Mu2eexperiment.

Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e