princeton-nlp / il-scaling-in-games

Official code repo of "Scaling Laws for Imitation Learning in NetHack"

Scaling Laws for Imitation Learning in NetHack

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.

NetHack Forecasted Model Weights

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 weights
• gdown 'https://drive.google.com/uc?id=1yyvbhq-yBF6q3lWCtfrIiyB1NdhTr5l2' for the model flags

Installation

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

Running the sample script

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.