hihack

This repo contains the official implementation of all methods and models from the NeurIPS 2023 paper "NetHack is Hard to Hack" (Piterbarg, Pinto, Fergus).

Installation

Please make sure to clone this repository recursively before attempting installation.

git clone --recursive git@github.com:upiterbarg/hihack.git

To install core dependencies with conda, run, conda env create -f conda_config.yaml

Next, to finish installation of the remaining dependencies on linux, run:

cd nle
python setup.py install
cd sys/unix && ./setup.sh && cd ../../..
conda install cmake
pip install git+ssh://git@github.com/facebookresearch/moolib
cd dungeonsdata-neurips2022/experiment_code
cd render_utils && pip install -e . && cd ..
pip install -e . && cd ../..

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Pre-trained Models

Pretrained checkpoints reflecting each of the model architectures (+ training paradigms) explored in our paper are available for download on the web via a single zip [1.24GB before inflating, 1.3GB after inflating].

wget horatio.cs.nyu.edu/mit/ulyana/hihack/pt_model_ckpts.zip && unzip pt_model_ckpts

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Evaluation

Code for evaluation with moolib (based on Hambro et al 2022) is provided in eval.py.

To launch a $NUM_ROLLOUTS-size evaluation of all pretrained model checkpoints (assuming these have been downloaded via the step above), run

python eval.py --model_name_or_path all -n $NUM_ROLLOUTS

By default, all final NLE scores/rewards from games played by each model will be saved to a text file in eval_results with corresponding name (e.g. final scores from games played by flat_transformer_bc.tar will be saved to eval_results/flat_transformer_bc.txt).

To launch a $NUM_ROLLOUTS-size evaluation of a single pretrained model checkpoint pass its alias to --model_name_or_path, e.g.,

python eval.py --model_name_or_path hier_trnsfrmr_bc -n $NUM_ROLLOUTS

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Data

Generating Strategy-Labeled NLE Data with AutoAscend

We've provided a script for multi-threaded hierarchical ttyrec data generation with AutoAscend, generate_data.py.

To generate $NUM_ROLLOUTS with $NUM_CORES, run

python generate_data.py -n $NUM_ROLLOUTS -c $NUM_CORES

By default, ttyrecs will be saved to data/test.

Full HiHack Dataset

The full HiHack dataset (~97GB, ~99GB after extraction) is now available for public download.

wget horatio.cs.nyu.edu/mit/ulyana/hihack/hihack_dataset.tar.gz && tar -xvzf hihack_dataset.tar.gz -C data/

Please be aware that download and extraction may take several hours, we recommend that these be run overnight.

Populating the .db file registering the hihack dataset via NLE-native dataloading functionality (lines 193-196 in experiment.py) may also take several hours. Please note that this operation only needs to be run once, as long as the location of hihack dataset files remain unchanged.

Sample HiHack Dataset

A (very) small HiHack-style sample dataset consisting of 31, strategy-labeled AutoAscend games can be found in data/toy_hihack. We also provide a simple Jupyter notebook loading and visualizing data from this sample as toy_hihack_explore.ipynb.

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Launching Experiments

To launch an experiment (i.e. train a model from scratch or with warmstarting, via BC or BC + APPO), first confirm all paths have been properly set in experiment_config.yaml. Then, run experiment.py via a moolib broker,

python -m moolib.broker &
echo -ne '\n' | sleep 5
export BROKER_IP=`hostname -I | cut -d' ' -f1`
export BROKER_PORT=4431
python experiment.py connect=$BROKER_IP:$BROKER_PORT

Experiment code is based on benchmarks introduced in Hambro et al 2022.