Generative design has been explored in architecture to produce unprecedented geometries, however, in most cases, design constraints are limited during the design process.
Limitations of existing generative design strategies include topological inconsistencies of the output geometries, dense design outputs, as the format used is often voxels or point clouds, and finally out-of-scope design constraints.
In order to overcome such shortcomings, a novel reinforcement learning (RL) framework is explored in order to design a series of furniture with embedded design and fabrication constraints.
You can try it out on our web application here!
- TensorFlow 1.14.0
- Stable Baselines 2.7.0
- OpenAI Gym 0.14.0
- Trimesh 3.2.13
- OpenMPI 3.2.1
