Probing the Structure of String Theory Vacua with Genetic Algorithms and Reinforcement Learning

Understand

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.