An Academy of Spatial Agents - Generating spatial configurations with deep reinforcement learning

Pedro Veloso, Ramesh Krishnamurti
2020 Proceedings of the 29th International Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe)   unpublished
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
more » ... 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.
doi:10.52842/conf.ecaade.2020.2.191 fatcat:lfaxyk2azzcutkfgzmsnjx53nm