Official code repo for the paper "Scaling Laws for Imitation Learning in NetHack". While we unfortunately cannot release the full code, we do release the model weights of our forecasted model below along with a sample script to run it.
Please download the model weights for our forecasted NetHack experiment from the following links:
- Model weights: https://drive.google.com/file/d/1tWxA92qkat7Uee8SKMNsj-BV1K9ENExl/view?usp=share_link
- Model flags: https://drive.google.com/file/d/1yyvbhq-yBF6q3lWCtfrIiyB1NdhTr5l2/view?usp=share_link
Make sure to place these files in a folder named nethack_files
in the root directory of this repo.
NOTE: You can use gdown
to download these using the command line. Simply install gdown
using pip install gdown
and then run the following commands:
gdown 'https://drive.google.com/uc?id=1tWxA92qkat7Uee8SKMNsj-BV1K9ENExl'
for the model weightsgdown 'https://drive.google.com/uc?id=1yyvbhq-yBF6q3lWCtfrIiyB1NdhTr5l2'
for the model flags
Clone the repository and run the following command in the root directory:
pip install -e .
Then run install from requirements.txt:
pip install -r requirements.txt
Once everything is installed, you can simply run
python3 -m il_scale.nethack.rollout --parameter_file conf/rollout_example.parameters
The hyperparameters in conf/rollout_example.parameters
are set to reproduce the numbers in the paper. You can also change the hyperparameters to run your own experiments.
NOTE: the num_actors
flag is currently set to 1. If you want to run multiple actors in parallel to speed things up (recommended), you can simply increase this number.