  We apply reinforcement learning (RL) to generate fine regular star
triangulations of reflexive polytopes, that give rise to smooth Calabi-Yau (CY)
hypersurfaces. We demonstrate that, by simple modifications to the data
encoding and reward function, one can search for CYs that satisfy a set of
desirable string compactification conditions. For instance, we show that our RL
algorithm can generate triangulations together with holomorphic vector bundles
that satisfy anomaly cancellation and poly-stability conditions in heterotic
compactification. Furthermore, we show that our algorithm can be used to search
for reflexive subpolytopes together with compatible triangulations that define
fibration structures of the CYs.
