  Data Quality Monitoring (DQM) is a crucial task in large particle physics
experiments, since detector malfunctioning can compromise the data. DQM is
currently performed by human shifters, which is costly and results in limited
accuracy. In this work, we provide a proof-of-concept for applying
human-in-the-loop Reinforcement Learning (RL) to automate the DQM process while
adapting to operating conditions that change over time. We implement a
prototype based on the Proximal Policy Optimization (PPO) algorithm and
validate it on a simplified synthetic dataset. We demonstrate how a multi-agent
system can be trained for continuous automated monitoring during data
collection, with human intervention actively requested only when relevant. We
show that random, unbiased noise in human classification can be reduced,
leading to an improved accuracy over the baseline. Additionally, we propose
data augmentation techniques to deal with scarce data and to accelerate the
learning process. Finally, we discuss further steps needed to implement the
approach in the real world, including protocols for periodic control of the
algorithm's outputs.
