  With this work, we investigate the use of Reinforcement Learning (RL) for the
generation of spatial assemblies, by combining ideas from Procedural Generation
algorithms (Wave Function Collapse algorithm (WFC)) and RL for Game Solving.
WFC is a Generative Design algorithm, inspired by Constraint Solving. In WFC,
one defines a set of tiles/blocks and constraints and the algorithm generates
an assembly that satisfies these constraints. Casting the problem of generation
of spatial assemblies as a Markov Decision Process whose states transitions are
defined by WFC, we propose an algorithm that uses Reinforcement Learning and
Self-Play to learn a policy that generates assemblies that maximize objectives
set by the designer. Finally, we demonstrate the use of our Spatial Assembly
algorithm in Architecture Design.
