We propose a Transformer based Reinforcement Learning Explorer (LB-Explorer) to search for heterotic standard models compactifying on smooth Calabi-Yau (CY) threefolds in the presence of Abelian internal gauge fields. We test the LB-Explorer environment on compactifications of $E_8\times E_8$ heterotic string theory on complete intersection Calabi-Yau (CICYs) manifolds to realize models with $SU(5)\times S(U(1)^5)$ symmetry whose $SU(5)$ can be further broken to MSSM-like gauge group using appropriate discrete Wilson lines. However, the Neural Network (NN) can in principle be applied to compactifications on any CYs that admit a smooth and simplicial Mori cone and has a freely-action symmetry. The LB-Explorer learns most of the constraints on the line bundle sums over the CY, guaranteeing the $E_8$ embedding of the gauge symmetry, the anomaly cancellation, supersymmetry preservation, chirality of the spectrum, and the absence of exotic matter. Knowing the action of the freely-acting symmetry, it is then possible to filter the solutions to impose the equivariant structure of the line bundle sum, while exact line bundle cohomologies will determine the particle spectrum exactly. The versatility and scalability of the LB-Explorer make it a powerful tool for exploring the string theory landscape with a large number of moduli.