This is our submission to the Minecraft Open-endedness challenge 2021 for the Gecco conference.
Our algorithm is based on Neural Cellular Automata (NCA), a CA-based neural network model inspired by morphogenesis. We chose to work with cellular automata, as we're interested in models where complexity can be spontaneously increasing over time, which is a property that traditional models (like neural networks) do not have. We train a NCA to grow different patterns from various seeds (or "genomes") in 2 or 3 dimensions. Once the training is done, we load the model in Minecraft and have players modify the genomes. They can be mutated or merged to create an endless stream of novel growing patterns. The resulting patterns depend both on the genome and the growth rules learned offline by the NCA, which can be unpredictable and surprising.
The repository contains link to pre-trained models in 2D and 3D as well as Colab notebooks links to train you own NCA.
The current codebase is very simplistic and therefore not very usable, because that's only what I needed to run my experiments and try several things. I plan to make it more usable in the near future.
Make sure you have the right Python packages installed. The project uses Poetry,
so you only need to do poetry install if you have it.
Otherwise pip install -r requirements.txt should work too.
Make also sure that you have installed the Evocraft Python interface to
Minecraft and your Minecraft server is
started with java -jar spongevanilla-1.12.2-7.3.0.jar.
The Pytorch models are defined in evo_ca/models.py.
To run a 2D model in Minecraft, use python run_2d.py <PATH>. For a 3D version
use python run_3d.py <PATH>, replacing <PATH> with a path to your model
weights.
The pre-trained weights are in a zip archive that you can download here. Unzip the archive in the repo root.
You can also train your own models with different target patterns in both 2D and 3D in the Colab notebooks