  We use deep reinforcement learning to explore a class of heterotic $SU(5)$
GUT models constructed from line bundle sums over Complete Intersection Calabi
Yau (CICY) manifolds. We perform several experiments where A3C agents are
trained to search for such models. These agents significantly outperform random
exploration, in the most favourable settings by a factor of 1700 when it comes
to finding unique models. Furthermore, we find evidence that the trained agents
also outperform random walkers on new manifolds. We conclude that the agents
detect hidden structures in the compactification data, which is partly of
general nature. The experiments scale well with $h^{(1,1)}$, and may thus
provide the key to model building on CICYs with large $h^{(1,1)}$.
