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NeurIPS 2021 - The NetHack Challenge - Starter Kit

This repository is the Nethack Challenge Starter kit! It contains:

  • Instructions for setting up your codebase to make submissions easy.
  • Baselines for quickly getting started training your agent.
  • Notebooks for introducing you to NetHack and the NLE.
  • Documentation for how to submit your model to the leaderboard.

Quick Links:

Quick Start

With Docker and x1 GPU

# 1. CLONE THE REPO AND DOWNLOAD BASELINE MODELS
git clone http://gitlab.aicrowd.com/nethack/neurips-2021-the-nethack-challenge.git \
    && cd neurips-2021-the-nethack-challenge \
    && git lfs install \
    && git lfs pull  

# 2. START THE DOCKER IMAGE
docker run -it -v `pwd`:/home/aicrowd --gpus 'all' fairnle/challenge:dev 

# 3. TEST AN EXISTING SUBMISSION 
python test_submission.py      # Tests ./saved_models/pretrained_0.5B

# 3. TRAIN YOUR OWN
python nethack_baselines/torchbeast/polyhydra.py batch_size=16

To Troubleshoot see here.

Table of Contents

  1. Intro to Nethack and the Nethack Challenge
  2. Setting up your codebase
  3. Baselines
  4. How to test and debug locally
  5. How to submit

Intro to Nethack and the Nethack Challenge

Your goal is to produce the best possible agent for navigating the depths of Nethack dungeons and emerging with the Amulet in hand! You can approach this task however you please, but a good starting point would be this notebook which provides an overview of (1) the many dynamics at play in the game and (2) the observation and action space with which your agent will interact.

A high level description of the Challenge Procedure:

  1. Sign up to join the competition on the AIcrowd website.
  2. Clone this repo and start developing your solution.
  3. Train your models on NLE, and ensure run.sh will generate rollouts.
  4. Submit your trained models to AIcrowd Gitlab for evaluation (full instructions below). The automated evaluation setup will evaluate the submissions against the NLE environment for a fixed number of rollouts to compute and report the metrics on the leaderboard of the competition.

Setting Up Your Codebase

AIcrowd provides great flexibility in the details of your submission!
Find the answers to FAQs about submission structure below, followed by the guide for setting up this starter kit and linking it to the AIcrowd GitLab.

FAQs

How does submission work?

The submission entrypoint is a bash script run.sh, that runs in an environment defined by Dockerfile. When this script is called, aicrowd will expect you to generate all your rollouts in the allotted time, using aicrowd_gym in place of regular gym. This means that AIcrowd can make sure everyone is running the same environment, and can keep score!

What languages can I use?

Since the entrypoint is a bash script run.sh, you can call any arbitrary code from this script. However, to get you started, the environment is set up to generate rollouts in Python.

The repo gives you a template placeholder to load your model (agents/your_agent.py), and a config to choose which agent to load (submission_config.py). You can then test a submission, adding all of AIcrowd’s timeouts on the environment, with python test_submission.py

How do I specify my dependencies?

We accept submissions with custom runtimes, so you can choose your favorite! The configuration files typically include requirements.txt (pypi packages), apt.txt (apt packages) or even your own Dockerfile.

You can check detailed information about the same in the RUNTIME.md file.

What should my code structure look like?

Please follow the example structure as it is in the starter kit for the code structure. The different files and directories have following meaning:

.
├── aicrowd.json                  # Submission meta information - add tags for tracks here
├── apt.txt                       # Packages to be installed inside submission environment
├── requirements.txt              # Python packages to be installed with pip
├── rollout.py                    # This will run rollouts on a batched agent
├── test_submission.py            # Run this on your machine to get an estimated score
├── run.sh                        # Submission entrypoint
├── utilities                     # Helper scripts for setting up and submission 
│   └── submit.sh                 # script for easy submission of your code
├── envs                          # Operations on the env like batching and wrappers
│   ├── batched_env.py            # Batching for multiple envs
│   └── wrappers.py   	          # Add wrappers to your env here
├── agents                        # Baseline agents for submission
│   ├── batched_agent.py          # Abstraction reference batched agents
│   ├── random_batched_agent.py	  # Batched agent that returns random actions
│   ├── rllib_batched_agent.py	  # Batched agent that runs with the rllib baseline
│   └── torchbeast_agent.py       # Batched agent that runs with the torchbeast baseline
├── nethack_baselines             # Baseline agents for submission
│    ├── other_examples  	
│    │   └── random_rollouts.py   # Barebones random agent with no batching
│    ├── rllib	                  # Baseline agent trained with rllib
│    └── torchbeast               # Baseline agent trained with IMPALA on Pytorch
└── notebooks                 
    └── NetHackTutorial.ipynb     # Tutorial on the Nethack Learning Environment

Finally, you must specify an AIcrowd submission JSON in aicrowd.json to be scored! See How do I actually make a submission? below for more details.

How can I get going with an existing baseline?

The best current baseline is the torchbeast baseline. Follow the instructions here to install and start training the model (there are even some suggestions for improvements).

To then submit your saved model, simply set the AGENT in submission config to be TorchBeastAgent, and modify the agent/torchbeast_agent.py to point to your saved directory.

You can now test your saved model with python test_submission.py

How can I get going with a completely new model?

Train your model as you like, and when you’re ready to submit, just adapt YourAgent in agents/your_agent.py to load your model and take a batched_step.

Then just set your AGENT in submission_config.py to be this class and you are ready to test with python test_submission.py

How do I actually make a submission?

First you need to fill in you aicrowd.json, to give AIcrowd some info so you can be scored. The aicrowd.json of each submission should contain the following content:

{
  "challenge_id": "neurips-2021-the-nethack-challenge",
  "authors": ["your-aicrowd-username"],
  "description": "(optional) description about your awesome agent",
  "gpu": true
}

The submission is made by adding everything including the model to git, tagging the submission with a git tag that starts with submission-, and pushing to AIcrowd's GitLab. The rest is done for you!

More details are available here.

Are there any hardware or time constraints?

Your submission will need to complete 128 rollouts in 30 minutes. We will run 4 of these in parallel, and a total of 512 episodes will be used for evaluation. The episode will timeout and terminate if any action is left hanging for 300 seconds, or 10,000 steps are taken without advancing the in game clock.

The machine where the submission will run will have following specifications:

  • 1 NVIDIA T4 GPU
  • 4 vCPUs
  • 16 GB RAM

Setting Up Details [No Docker]

  1. Add your SSH key to AIcrowd GitLab

    You can add your SSH Keys to your GitLab account by going to your profile settings here. If you do not have SSH Keys, you will first need to generate one.

  2. Clone the repository

    git clone [email protected]:nethack/neurips-2021-the-nethack-challenge.git

  3. Verify you have dependencies for the Nethack Learning Environment

    NLE requires python>=3.5, cmake>=3.14 to be installed and available both when building the package, and at runtime.

    On MacOS, one can use Homebrew as follows:

    brew install cmake

    On a plain Ubuntu 18.04 distribution, cmake and other dependencies can be installed by doing:

    # Python and most build deps
sudo apt-get install -y build-essential autoconf libtool pkg-config \
    python3-dev python3-pip python3-numpy git flex bison libbz2-dev

# recent cmake version
wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | sudo apt-key add -
sudo apt-add-repository 'deb https://apt.kitware.com/ubuntu/ bionic main'
sudo apt-get update && apt-get --allow-unauthenticated install -y \
    cmake \
    kitware-archive-keyring

  4. Install competition specific dependencies!

    We advise using a conda environment for this:

    # Optional: Create a conda env
conda create -n nle_challenge python=3.8 'cmake>=3.15'
conda activate nle_challenge
pip install -r requirements.txt

    If pip install fails with errors when installing NLE, please see installation requirements at https://github.com/facebookresearch/nle.

  5. Run rollouts with a random agent with python test_submission.py.

    Find more details on the original nethack repository

Setting Up Details [Docker]

With Docker, setting up is very simple! Simply pull a preexisting image from the fair nle repo.

docker pull fairnle/challenge:dev

This image is based of Ubuntu 18.04, with CUDA 10.2 and cudnn 7, and is the Docker image corresponding to the nhc-dev target in the Dockerfile. You can run it as follows:

Without GPUS

docker run -it -v `pwd`:/home/aicrowd fairnle/challenge:dev

With GPUS

docker run -it -v `pwd`:/home/aicrowd --gpus 'all' fairnle/challenge:dev

NB On Linux, this --gpus argument requires you to install nvidia-container-toolkit, which on Ubuntu is available with apt install.

This will take you into an image, with your current working directory mounted as a volume. At submission time, nhc-submit target will be built by AIcrowd, which copies all the files into the image, instead of simply mounting them.

If you wish to wish to build your own dev environment from the Dockerfile, you can do this with:

docker build --target nhc-dev  -t your-image-name .

Baselines

Although we are looking to supply this repository with more baselines throughout the first month of the competition, this repository comes with a strong IMPALA-based baseline in the directory ./nethack_baselines/torchbeast.

The README has more info about the baselines, including to install and start training the model (there are even some suggestions for improvements).

The TorchBeast baseline comes with two sets of weights - the same model trained to 250 million steps, and 500 million steps.

To download these weights, run git lfs pull, and check saved_models.

The TorchBeast agent can then be selected by setting AGENT=TorchBeastAgent in the submission_config.py, and the weights can be changed by changing the MODEL_DIR in agents/torchbeast_agent.py.

More information on git lfs can be found on SUBMISSION.md.

How to Test and Debug Locally

The best way to test your model is to run your submission locally.

You can do this naively by simply running python rollout.py or you can simulate the extra timeout wrappers that AIcrowd will implement by using python test_submission.py.

How to Submit

More information on submissions can be found at our SUBMISSION.md.

Contributors

📎 Important links

Best of Luck 🎉 🎉

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RAPH - Reinforcement Agent Playing netHack

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