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OMNI: Open-endedness via Models of human Notions of Interestingness [Arxiv] [Website] [Tweet]

https://github.com/jennyzzt/omni/assets/53294998/a681f581-58ad-4b7f-b365-3c8505d697cf

This is the source code repository for the OMNI: Open-endedness via Models of human Notions of Interestingness paper. OMNI utilizes large (language) models as a model of interestingness, because they already internalize human concepts of interestingness from training on vast amounts of human-generated data. This repository implements OMNI on a procedurally generated 2D gridworld gomain Crafter.

Code Layout

Setup

Clone the repository with git clone <repo_url> && cd omni_code.
Create python virtual environment python3 -m venv venv.
Activate python virtual environment source venv/bin/activate.
Install dependencies pip install -r requirements.txt.

Training

Run train.py script with the necessary args:

python train.py --model <model_name> --env <env_name>

For example, in the repetitive task setting
Uniform: python train.py --model tr_uni-1 --env tr_uni --seed 1
LP: python train.py --model tr_lp-1 --env tr_lp --seed 1
OMNI: python train.py --model tr_omni-1 --env tr_omni --seed 1

Evaluation

Run evaluate.py script with the necessary args:

python evaluate.py --model <model_name> --env <env_name>

Crafter GUI

Run crafter/run_gui.py script with the necessary args:

python crafter/run_gui.py --env <env_name>

Acknowledgements

This codebase draws inspiration from the following codebases:

Citation

@article{zhang2023omni,
  title={OMNI: Open-endedness via Models of human Notions of Interestingness},
  author={Jenny Zhang and Joel Lehman and Kenneth Stanley and Jeff Clune},
  year={2023},
  journal={arXiv preprint arXiv:2306.01711},
}