  We use reinforcement learning as a means of constructing string
compactifications with prescribed properties. Specifically, we study heterotic
SO(10) GUT models on Calabi-Yau three-folds with monad bundles, in search of
phenomenologically promising examples. Due to the vast number of bundles and
the sparseness of viable choices, methods based on systematic scanning are not
suitable for this class of models. By focusing on two specific manifolds with
Picard numbers two and three, we show that reinforcement learning can be used
successfully to explore monad bundles. Training can be accomplished with
minimal computing resources and leads to highly efficient policy networks. They
produce phenomenologically promising states for nearly 100% of episodes and
within a small number of steps. In this way, hundreds of new candidate standard
models are found.
