  Identifying string theory vacua with desired physical properties at low
energies requires searching through high-dimensional solution spaces -
collectively referred to as the string landscape. We highlight that this search
problem is amenable to reinforcement learning and genetic algorithms. In the
context of flux vacua, we are able to reveal novel features (suggesting
previously unidentified symmetries) in the string theory solutions required for
properties such as the string coupling. In order to identify these features
robustly, we combine results from both search methods, which we argue is
imperative for reducing sampling bias.
