The Pennsylvania State University received a $364,032 federal Project Grant award from the National Science Foundation (NSF) Engineering (CFDA 47.041) program. The aim of this 3-year award is to derive a rigorous and highly effective discrete structural optimization framework by converging structural optimization principles and sequential decision-making algorithms. The research will explore how to define Markov decision process actions to accommodate discrete design variables representing standardized structural elements, investigate tailored deep reinforcement learning solution architectures, extend the framework to volume minimization problems, and validate the solutions through design examples. Selected design examples will also be integrated into an educational application/software where the design of truss and frame structures is presented as a game based on sequential decision-making. This work is intended to improve design outcomes in fields like civil, aerospace, and mechanical engineering by identifying novel and efficient solutions that can reduce resource consumption, embodied carbon, and enhance safety, serviceability, and aesthetics.