  Modern radio telescopes produce unprecedented amounts of data, which are
passed through many processing pipelines before the delivery of scientific
results. Hyperparameters of these pipelines need to be tuned by hand to produce
optimal results. Because many thousands of observations are taken during a
lifetime of a telescope and because each observation will have its unique
settings, the fine tuning of pipelines is a tedious task. In order to automate
this process of hyperparameter selection in data calibration pipelines, we
introduce the use of reinforcement learning. We test two reinforcement learning
techniques, twin delayed deep deterministic policy gradient (TD3) and soft
actor-critic (SAC), to train an autonomous agent to perform this fine tuning.
For the sake of generalization, we consider the pipeline to be a black-box
system where the summarized state of the performance of the pipeline is used by
the autonomous agent. The autonomous agent trained in this manner is able to
determine optimal settings for diverse observations and is therefore able to
perform 'smart' calibration, minimizing the need for human intervention.
