Background and aim:
Spatial qualities heavily depend on the configuration of spaces. In this research project, we develop computational agents that configures the space within a voxelated envelope while ensuring spatial qualities such as daylight, accessibility to other spaces, etc., via Multi-Criteria Decision Analysis. Each computational agent utilizes Reinforcement Learning to understand the inter-relation of global spatial quality criteria with local spatial decisions.
Research question:
- How to train an ensemble of artificial agents to make local spatial decisions to attain high global spatial qualities?
Design objective:
- To design and implement a computational 3D layout methodology using DRL.
Methods:
- Deep Reinforcement Learning (DRL, Artificial Intelligence)
- Multi-Criteria Decision Analysis (MCDA)
- Topology Optimization
- Computer Programming (Python/C#)