  The organising principles underlying the structure of phenomenologically
viable string vacua can be accessed by sampling such vacua. In many cases this
is prohibited by the computational cost of standard sampling methods in the
high dimensional model space. Here we show how this problem can be alleviated
using reinforcement learning techniques to explore string flux vacua. We
demonstrate in the case of the type IIB flux landscape that vacua with
requirements on the expectation value of the superpotential and the string
coupling can be sampled significantly faster by using reinforcement learning
than by using metropolis or random sampling. Our analysis is on conifold and
symmetric torus background geometries. We show that reinforcement learning is
able to exploit successful strategies for identifying such phenomenologically
interesting vacua. The strategies are interpretable and reveal previously
unknown correlations in the flux landscape.
