  We introduce a novel framework for studying small-scale primordial
perturbations and their cosmological implications. The framework uses a deep
reinforcement learning to generate scalar power spectrum profiles that are
consistent with current observational constraints. The framework is shown to
predict the abundance of primordial black holes and the production of secondary
induced gravitational waves. We demonstrate that the set up under consideration
is capable of generating predictions that are beyond the traditional
model-based approaches.
