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BetsyJin committed Nov 29, 2020
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41 changes: 30 additions & 11 deletions README.md
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# SOFA
This repository is the implementation of SOFA, the Simulator for OFfline leArning and evaluation.

### Files in the folder

### Required packages
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### Running the code

### More details
# SOFA
This repository is the implementation of [SOFA](https://staff.fnwi.uva.nl/m.derijke/wp-content/papercite-data/pdf/huang-2020-keeping.pdf), the Simulator for OFfline leArning and evaluation.
> Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. Jin Huang, Harrie Oosterhuis, Maarten de Rijke, Herke van Hoof. Recsys 2020.
<!-- ### Files in the folder -->

<!-- ### Required packages -->

## Running the code
```
$ cd examples
$ python run_dqn.py
```

## More details
We also provide the details of DQN-based Policy used in experiments and the related hyperparamters (See Appendix).

## Cite
If you use our code, please cite our paper:
```
@inproceedings{huang2020keeping,
title={Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems},
author={Huang, Jin and Oosterhuis, Harrie and de Rijke, Maarten and van Hoof, Herke},
booktitle={Fourteenth ACM Conference on Recommender Systems},
pages={190--199},
year={2020}
}
```
96 changes: 96 additions & 0 deletions requirements.txt
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# This file may be used to create an environment using:
# $ conda create --name <env> --file <this file>
# platform: linux-64
_libgcc_mutex=0.1=main
_tflow_select=2.1.0=gpu
absl-py=0.8.1=py36_0
astor=0.8.0=py36_0
attrs=19.3.0=py_0
blas=1.0=mkl
c-ares=1.15.0=h7b6447c_1001
ca-certificates=2019.11.27=0
certifi=2019.11.28=py36_0
cffi=1.13.2=py36h2e261b9_0
cudatoolkit=9.2=0
cudnn=7.6.4=cuda9.2_0
cupti=9.2.148=0
cycler=0.10.0=py36_0
dbus=1.13.12=h746ee38_0
expat=2.2.6=he6710b0_0
fontconfig=2.13.0=h9420a91_0
freetype=2.9.1=h8a8886c_1
gast=0.3.2=py_0
glib=2.63.1=h5a9c865_0
grpcio=1.16.1=py36hf8bcb03_1
gst-plugins-base=1.14.0=hbbd80ab_1
gstreamer=1.14.0=hb453b48_1
h5py=2.9.0=py36h7918eee_0
hdf5=1.10.4=hb1b8bf9_0
icu=58.2=h9c2bf20_1
importlib_metadata=1.3.0=py36_0
intel-openmp=2019.4=243
joblib=0.14.1=py_0
jpeg=9b=h024ee3a_2
keras-applications=1.0.8=py_0
keras-preprocessing=1.1.0=py_1
kiwisolver=1.1.0=py36he6710b0_0
libedit=3.1.20181209=hc058e9b_0
libffi=3.2.1=hd88cf55_4
libgcc-ng=9.1.0=hdf63c60_0
libgfortran-ng=7.3.0=hdf63c60_0
libpng=1.6.37=hbc83047_0
libprotobuf=3.10.1=hd408876_0
libstdcxx-ng=9.1.0=hdf63c60_0
libuuid=1.0.3=h1bed415_2
libxcb=1.13=h1bed415_1
libxml2=2.9.9=hea5a465_1
markdown=3.1.1=py36_0
matplotlib=3.1.1=py36h5429711_0
mkl=2019.4=243
mkl-service=2.3.0=py36he904b0f_0
mkl_fft=1.0.15=py36ha843d7b_0
mkl_random=1.1.0=py36hd6b4f25_0
more-itertools=8.0.2=py_0
ncurses=6.1=he6710b0_1
ninja=1.9.0=py36hfd86e86_0
numpy=1.16.0=py36h7e9f1db_1
numpy-base=1.16.0=py36hde5b4d6_1
openssl=1.1.1d=h7b6447c_3
packaging=20.0=py_0
pandas=0.25.3=py36he6710b0_0
pcre=8.43=he6710b0_0
pip=19.3.1=py36_0
pluggy=0.13.1=py36_0
protobuf=3.10.1=py36he6710b0_0
py=1.8.1=py_0
pycparser=2.19=py36_0
pyparsing=2.4.6=py_0
pyqt=5.9.2=py36h05f1152_2
pytest=5.3.2=py36_0
python=3.6.9=h265db76_0
python-dateutil=2.8.1=py_0
pytorch=1.3.1=py3.6_cuda9.2.148_cudnn7.6.3_0
pytz=2019.3=py_0
qt=5.9.7=h5867ecd_1
readline=7.0=h7b6447c_5
recnn=0.1=pypi_0
scikit-learn=0.22=py36hd81dba3_0
scipy=1.3.1=py36h7c811a0_0
setuptools=41.6.0=py36_0
sip=4.19.8=py36hf484d3e_0
six=1.13.0=py36_0
sqlite=3.30.1=h7b6447c_0
tensorboard=1.12.2=py36he6710b0_0
tensorflow=1.12.0=gpu_py36he74679b_0
tensorflow-base=1.12.0=gpu_py36had579c0_0
tensorflow-gpu=1.12.0=h0d30ee6_0
termcolor=1.1.0=py36_1
tk=8.6.8=hbc83047_0
tornado=6.0.3=py36h7b6447c_0
tqdm=4.40.2=py_0
wcwidth=0.1.7=py36_0
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wheel=0.33.6=py36_0
xz=5.2.4=h14c3975_4
zipp=0.6.0=py_0
zlib=1.2.11=h7b6447c_3

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