Equivariant Networks for Zero-Shot Coordination

Introduction

This repo contains the implementation of the EQC architecture proposed in Equivariant Networks for Zero-Shot Coordination, NeurIPS 2022.

This repo is largely based off the Off-Belief Learning repo and contains the implementation of the algorithm proposed in Off-Belief Learning, ICML 2021. As such, much of the README from that repo is the same here.

Environment Setup

We have been using pytorch-1.5.1, cuda-10.1, and cudnn-v7.6.5 in our development environment. We have not tested it extensively in other environment configurations but it may also work. You will need to change the pybind submodule to the same version as the one used by your pytorch, which is detailed in later section. We also use conda/miniconda to manage environments.

conda create -n hanabi python=3.7
conda activate hanabi

# install pytorch
# the code was developed with pytorch 1.5.1, but newer versions may also work
pip install torch==1.5.1+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html

# install other dependencies
pip install psutil

# install a newer cmake if the current version is < 3.15
conda install -c conda-forge cmake

To help cmake find the proper libraries (e.g. libtorch), please either add the following lines to your .bashrc, or add it to a separate file and source it before you start working on the project.

# activate the conda environment
conda activate hanabi

# set path
CONDA_PREFIX=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
export CPATH=${CONDA_PREFIX}/include:${CPATH}
export LIBRARY_PATH=${CONDA_PREFIX}/lib:${LIBRARY_PATH}
export LD_LIBRARY_PATH=${CONDA_PREFIX}/lib:${LD_LIBRARY_PATH}

# avoid tensor operation using all cpu cores
export OMP_NUM_THREADS=1

If you use a newer version of pytorch (e.g. >= v1.9), first check out the pybind module to use the corresponding version (the version can be found at pybind11 row here):

cd third_party/pybind11
git checkout v2.6.2
cd ../..

Finally, to compile this repo:

# under project root
mkdir build
cd build
cmake ..
make -j10

Code Structure

For an overview of the training infrastructure, please refer to Figure 5 of the [Off-Belief Learning] (https://arxiv.org/pdf/2103.04000.pdf) paper.

hanabi-learning-environment is a modified version of the original HLE from Deepmind.

Notable modifications includes:

1. Card knowledge part of the observation encoding is changed to v0-belief, i.e. card knowledge normalized by the remaining public card count.

2. Functions to reset the game state with sampled hands.

rela (REinforcement Learning Assemly) is a set of tools for efficient batched neural network inference written in C++ with multi-threading.

rlcc implements the core of various algorithms. For example, the logic of fictitious transitions are implemented in r2d2_actor.cc. It also contains implementations of baselines such as other-play, VDN and IQL.

pyhanabi is the main entry point of the repo. It contains implementations for Q-network, recurrent DQN training, belief network and training, as well as some tools to analyze trained models.

Run the Code

Please refer to the README in pyhanabi for detailed instruction on how to train a model.

Download Models

To download the trained models used in the paper, go to models folder and run

sh download.sh

Due to agreement with BoardGameArena and Facebook policies, we are unable to release the "Clone Bot" models trained on the game data nor the datasets themselves.

Copyright

Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.

This source code is licensed under the license found in the LICENSE file in the root directory of this source tree.