Off-Belief Learning
Introduction
This repo contains the implementation of the algorithm proposed in Off-Belief Learning, ICML 2021.
Environment Setup
We have been using pytorch-1.5.1, cuda-10.1, and cudnn-v7.6.5 in our development environment. Other settings may also work but we have not tested it extensively under different configurations. We also use conda/miniconda to manage environments.
There are known issues when using this repo with newer versions of pytorch, such as this illegal move issue.
<div class="highlight highlight-source-shell position-relative overflow-auto" data-snippet-clipboard-copy-content="conda create -n hanabi python=3.7
conda activate hanabi
# install pytorch 1.5.1
# note that newer versions may cause compilation issues
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
conda create -n hanabi python=3.7
conda activate hanabi
# install pytorch 1.5.1
# note that newer versions may cause compilation issues
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
Finally, to compile this repo:
# under project root
mkdir build
cd build
cmake ..
make -j10
Code Structure
For an overview of how the training infrastructure, please refer to Figure 5 of the Off-Belief Learning paper.
hanabi-learning-environment is a modified version of the original HLE from Deepmind.
Notable modifications includes:
-
Card knowledge part of the observation encoding is changed to v0-belief, i.e. card knowledge normalized by the remaining public card count.
-
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
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.
GitHub
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