Autonomous robotic assembly requires a well-orchestrated sequence of high-level actions and smooth manipulation executions. The problem of learning to assemble complex 3D structures remains challenging, as it requires drawing connections between target shapes and available building blocks, as well as creating valid assembly sequences with respect to stability and kinematic feasibility in the robot’s workspace. We design a hierarchical control framework that learns to sequence the building blocks to construct arbitrary 3D designs and ensures that they are feasible, as we plan the geometric execution with the robot-in-the-loop. Our approach draws its generalization properties from combining graph-based representations with reinforcement learning (RL) and ultimately adding tree-search. Combining structured representations with model-free RL and Monte-Carlo planning allows agents to operate with various target shapes and building block types. We demonstrate the flexibility of the proposed structured representation and our algorithmic solution in a series of simulated 3D assembly tasks with robotic evaluation, which showcases our method’s ability to learn to construct stable structures with a large number of building blocks.