Training Spaces - Fostering machine sensibility for spatial assemblages through wave function collapse and reinforcement learning

This research explores the integration of Deep Reinforcement Learning (RL) and a Wave Function Collapse (WFC) algorithm for a goal-driven, open-ended generation of architectural spaces. Our approach binds RL to a distributed network of decisions, unfolding through three key steps: the definition of a set of architectural components (tiles) and their connectivity rules, the selection of the tile placement location, which is determined by the WFC, and the choice of which tile to place, which is performed by RL. The act of thinking becomes granular and embedded in an iterative process, distributed among human and non-human cognition, which constantly negotiate their agency and authorial status. Tools become active agents capable of developing their own sensibility while controlling specific spatial conditions. Establishing an interdependency with the human, that engenders the design patterns and becomes an indispensable prerequisite for the exploration of the generated design space, exceeding human or machinic reach alone.