Tigas, Panagiotis;
Hosmer, Tyson;
(2020)
Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search.
In:
34rd Conference on Neural Information Processing Systems (NeurIPS 2020).
NeurIPS
(In press).
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Abstract
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.
Type: | Proceedings paper |
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Title: | Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search |
Event: | Workshop on Machine Learning for Creativity and Design at the 34rd Conference on Neural Information Processing Systems (NeurIPS 2020) |
Dates: | 6 Dec 2020 - 12 Dec 2020 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.48550/arXiv.2101.07579 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > The Bartlett School of Architecture |
URI: | https://discovery.ucl.ac.uk/id/eprint/10162685 |
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