The current machine learning paradigm relies on continuous representations and fixed neural network architectures to approximate environmental structures, leading to challenges with continual learning, internal structure design, and goal-directed behavior due to overparameterization and reliance on continuous parameter tuning. This paper introduces “Modelleyen,” an alternative learning mechanism that learns environmental structures topologically in an inherently continual manner, and a planning algorithm that utilizes Modelleyen’s output for goal-directed behavior. We demonstrate the effectiveness of Modelleyen and the planner in a simple environment, and also discuss their potential for creating human-comprehensible hierarchical models in machine learning.