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With this work, we investigate the use of Reinforcement Learning (RL) for thegeneration of spatial assemblies, by combining ideas from Procedural Generationalgorithms (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 generatesan assembly that satisfies these constraints. Casting the problem of generationof spatial assemblies as a Markov Decision Process whose states transitions aredefined by WFC, we propose an algorithm that uses Reinforcement Learning andSelf-Play to learn a policy that generates assemblies that maximize objectivesset by the designer. Finally, we demonstrate the use of our Spatial Assemblyalgorithm in Architecture Design.

Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search