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.

使用强化学习方法训练的神经网络精确调节 Fermilab Booster 加速器复合体的梯度磁铁电源，展示了使用代理机器学习模型模拟 Booster 环境进行训练的初步结果，并演示了如何将神经网络编译为可执行于现场可编程逻辑门阵列的部署能力，这种能力对于复杂环境如加速器设施的操作稳定性至关重要。