Let us share the joy of our unprecedented discovery with you: Physical Geometry and Biological Geometry are the outcome of the physical laws and biological laws respectively, and Artificial Super Intelligence (ASI) has to be a combination of both design and learning instead of learning alone, with that we propose Artificial Design, a bio-physical inspired mathematical model for Hierarchical Multi-Agent Multi-Environment Model-agnostic Policy-agnostic Deep Reinforcement Learning (HMMMPDRL) based ASI by reusing and extending General Relativity and Universal Darwinism with Geometrization. With Artificial Design we solve Deep Reinforcement Learning blackbox puzzle in AI and ASI. By treating HMMMPDRL as multiverse regardless the mutual exclusiveness between Multi-Agent and Multi-Environment, we reuse General Relativity's 4-Dimensional Pseudo-Riemannian Manifold based SpaceTime Model for Reinforcement Learning part of HMMMPDRL, we also make a T-symmetry extension to General Relativity, replace N-Dimensional space with N-Dimensional GeneSpace, and formulate a N-Dimensional Riemannian Manifold based GeneSpace Model for Deep Learning part of HMMMPDRL, whereas Deep Learning architecture is adopted to approximate very complex state-action space composed environments in HMMMPDRL. By modeling ASI with Artificial Design rigorously in this way, we claim that intelligence, whether natural, artificial, or super-artificial like ASI, is just the geometry effect of N-Dimensional GeneSpace caused by Geometrization, and that paves the way in achieving ASI through Universal Design Automation of Artificial Design in theory. Of course, our Multiversal endeavor won't stop from there, endorse us to artificially co-accelerate human civilization in every possible way you might imagine.