  To improve the performance-critical stability and brightness of the electron
bunch at injection into the proton-driven plasma wakefield at the AWAKE CERN
experiment, automation approaches based on unsupervised Machine Learning (ML)
were developed and deployed. Numerical optimisers were tested together with
different model-free reinforcement learning agents. In order to avoid any bias,
reinforcement learning agents have been trained also using a completely
unsupervised state encoding using auto-encoders. To aid hyper-parameter
selection, a full synthetic model of the beamline was constructed using a
variational auto-encoder trained to generate surrogate data from equipment
settings. This paper describes the novel approaches based on deep learning and
reinforcement learning to aid the automatic setup of a low energy line, as the
one used to deliver beam to the AWAKE facility. The results obtained with the
different ML approaches, including automatic unsupervised feature extraction
from images using computer vision are presented. The prospects for operational
deployment and wider applicability are discussed.
