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.
翻译:通过这项工作,我们通过将程序生成算法(Wave 函数折叠算法(WFC))和游戏解决方案(RL)的理念结合起来,调查使用强化学习(RL)生成空间组件的问题。WFC是一种由约束性溶解所启发的创造型设计算法。在WFC中,我们定义了一组砖块/块和制约,而算法产生一个能够满足这些限制的组装。将空间组件的生成问题作为一个马尔科夫决策程序,由WFC确定各州的过渡,我们提出一个使用强化学习和自我促进的算法,以学习产生能够使设计者设定的目标最大化的组合的政策。最后,我们展示了在建筑设计中使用空间组合算法的情况。