| id |
ecaade2020_093 |
| authors |
Veloso, Pedro and Krishnamurti, Ramesh |
| year |
2020 |
| title |
An Academy of Spatial Agents - Generating spatial configurations with deep reinforcement learning |
| source |
Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 191-200 |
| doi |
https://doi.org/10.52842/conf.ecaade.2020.2.191
|
| summary |
Agent-based models rely on decentralized decision making instantiated in the interactions between agents and the environment. In the context of generative design, agent-based models can enable decentralized geometric modelling, provide partial information about the generative process, and enable fine-grained interaction. However, the existing agent-based models originate from non-architectural problems and it is not straight-forward to adapt them for spatial design. To address this, we introduce a method to create custom spatial agents that can satisfy architectural requirements and support fine-grained interaction using multi-agent deep reinforcement learning (MADRL). We focus on a proof of concept where agents control spatial partitions and interact in an environment (represented as a grid) to satisfy custom goals (shape, area, adjacency, etc.). This approach uses double deep Q-network (DDQN) combined with a dynamic convolutional neural-network (DCNN). We report an experiment where trained agents generalize their knowledge to different settings, consistently explore good spatial configurations, and quickly recover from perturbations in the action selection. |
| keywords |
space planning; agent-based model; interactive generative systems; artificial intelligence; multi-agent deep reinforcement learning |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (14,078,165 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:58 |
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