Addressing Large Action Spaces in 3D Floorplanning via Spatial Generalization

ArXi:2406.10538v3 Announce Type: replace Many recent machine learning approaches to floorplanning represent placement decisions using discrete canvas coordinates, which creates scalability bottlenecks as the action space grows. In this work, we study the effect of learning a continuous action representation for 3D floorplanning. By reasoning in a continuous placement space and discretizing only at inference time, our method decouples the output structure from the canvas resolution, which makes learning and inference tractable in large design spaces.