%global _empty_manifest_terminate_build 0
Name: python-open-spiel
Version: 1.3
Release: 1
Summary: A Framework for Reinforcement Learning in Games
License: Apache 2.0
URL: https://github.com/deepmind/open_spiel
Source0: https://mirrors.aliyun.com/pypi/web/packages/ab/68/b408ada0202d52238d07e0de52de1098ac65c1ff25ddbf5c0da02318d3b2/open_spiel-1.3.tar.gz
BuildArch: noarch
Requires: python3-pip
Requires: python3-attrs
Requires: python3-absl-py
Requires: python3-numpy
Requires: python3-scipy
%description
# OpenSpiel: A Framework for Reinforcement Learning in Games
[](https://openspiel.readthedocs.io/en/latest/?badge=latest)
OpenSpiel is a collection of environments and algorithms for research in general
reinforcement learning and search/planning in games. OpenSpiel supports n-player
(single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and
sequential, strictly turn-taking and simultaneous-move, perfect and imperfect
information games, as well as traditional multiagent environments such as
(partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel
also includes tools to analyze learning dynamics and other common evaluation
metrics. Games are represented as procedural extensive-form games, with some
natural extensions. The core API and games are implemented in C++ and exposed to
Python. Algorithms and tools are written both in C++ and Python.
To try OpenSpiel in Google Colaboratory, please refer to `open_spiel/colabs` subdirectory or start [here](https://colab.research.google.com/github/deepmind/open_spiel/blob/master/open_spiel/colabs/install_open_spiel.ipynb).
# Index
Please choose among the following options:
* [Installing OpenSpiel](docs/install.md)
* [Introduction to OpenSpiel](docs/intro.md)
* [API Overview and First Example](docs/concepts.md)
* [API Reference](docs/api_reference.md)
* [Overview of Implemented Games](docs/games.md)
* [Overview of Implemented Algorithms](docs/algorithms.md)
* [Developer Guide](docs/developer_guide.md)
* [Using OpenSpiel as a C++ Library](docs/library.md)
* [Guidelines and Contributing](docs/contributing.md)
* [Authors](docs/authors.md)
For a longer introduction to the core concepts, formalisms, and terminology,
including an overview of the algorithms and some results, please see
[OpenSpiel: A Framework for Reinforcement Learning in Games](https://arxiv.org/abs/1908.09453).
For an overview of OpenSpiel and example uses of the core API, please check out
our tutorials:
* [Motivation, Core API, Brief Intro to Replictor Dynamics and Imperfect
Information Games](https://www.youtube.com/watch?v=8NCPqtPwlFQ) by Marc
Lanctot.
[(slides)](http://mlanctot.info/files/OpenSpiel_Tutorial_KU_Leuven_2022.pdf)
[(colab)](https://colab.research.google.com/github/deepmind/open_spiel/blob/master/open_spiel/colabs/OpenSpielTutorial.ipynb)
* [Motivation, Core API, Implementing CFR and REINFORCE on Kuhn poker, Leduc
poker, and Goofspiel](https://www.youtube.com/watch?v=o6JNHoGUXCo) by Edward
Lockhart.
[(slides)](http://mlanctot.info/files/open_spiel_tutorial-mar2021-comarl.pdf)
[(colab)](https://colab.research.google.com/github/deepmind/open_spiel/blob/master/open_spiel/colabs/CFR_and_REINFORCE.ipynb)
If you use OpenSpiel in your research, please cite the paper using the following
BibTeX:
```bibtex
@article{LanctotEtAl2019OpenSpiel,
title = {{OpenSpiel}: A Framework for Reinforcement Learning in Games},
author = {Marc Lanctot and Edward Lockhart and Jean-Baptiste Lespiau and
Vinicius Zambaldi and Satyaki Upadhyay and Julien P\'{e}rolat and
Sriram Srinivasan and Finbarr Timbers and Karl Tuyls and
Shayegan Omidshafiei and Daniel Hennes and Dustin Morrill and
Paul Muller and Timo Ewalds and Ryan Faulkner and J\'{a}nos Kram\'{a}r
and Bart De Vylder and Brennan Saeta and James Bradbury and David Ding
and Sebastian Borgeaud and Matthew Lai and Julian Schrittwieser and
Thomas Anthony and Edward Hughes and Ivo Danihelka and Jonah Ryan-Davis},
year = {2019},
eprint = {1908.09453},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
journal = {CoRR},
volume = {abs/1908.09453},
url = {http://arxiv.org/abs/1908.09453},
}
```
## Versioning
We use [Semantic Versioning](https://semver.org/).
%package -n python3-open-spiel
Summary: A Framework for Reinforcement Learning in Games
Provides: python-open-spiel
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-open-spiel
# OpenSpiel: A Framework for Reinforcement Learning in Games
[](https://openspiel.readthedocs.io/en/latest/?badge=latest)
OpenSpiel is a collection of environments and algorithms for research in general
reinforcement learning and search/planning in games. OpenSpiel supports n-player
(single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and
sequential, strictly turn-taking and simultaneous-move, perfect and imperfect
information games, as well as traditional multiagent environments such as
(partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel
also includes tools to analyze learning dynamics and other common evaluation
metrics. Games are represented as procedural extensive-form games, with some
natural extensions. The core API and games are implemented in C++ and exposed to
Python. Algorithms and tools are written both in C++ and Python.
To try OpenSpiel in Google Colaboratory, please refer to `open_spiel/colabs` subdirectory or start [here](https://colab.research.google.com/github/deepmind/open_spiel/blob/master/open_spiel/colabs/install_open_spiel.ipynb).
# Index
Please choose among the following options:
* [Installing OpenSpiel](docs/install.md)
* [Introduction to OpenSpiel](docs/intro.md)
* [API Overview and First Example](docs/concepts.md)
* [API Reference](docs/api_reference.md)
* [Overview of Implemented Games](docs/games.md)
* [Overview of Implemented Algorithms](docs/algorithms.md)
* [Developer Guide](docs/developer_guide.md)
* [Using OpenSpiel as a C++ Library](docs/library.md)
* [Guidelines and Contributing](docs/contributing.md)
* [Authors](docs/authors.md)
For a longer introduction to the core concepts, formalisms, and terminology,
including an overview of the algorithms and some results, please see
[OpenSpiel: A Framework for Reinforcement Learning in Games](https://arxiv.org/abs/1908.09453).
For an overview of OpenSpiel and example uses of the core API, please check out
our tutorials:
* [Motivation, Core API, Brief Intro to Replictor Dynamics and Imperfect
Information Games](https://www.youtube.com/watch?v=8NCPqtPwlFQ) by Marc
Lanctot.
[(slides)](http://mlanctot.info/files/OpenSpiel_Tutorial_KU_Leuven_2022.pdf)
[(colab)](https://colab.research.google.com/github/deepmind/open_spiel/blob/master/open_spiel/colabs/OpenSpielTutorial.ipynb)
* [Motivation, Core API, Implementing CFR and REINFORCE on Kuhn poker, Leduc
poker, and Goofspiel](https://www.youtube.com/watch?v=o6JNHoGUXCo) by Edward
Lockhart.
[(slides)](http://mlanctot.info/files/open_spiel_tutorial-mar2021-comarl.pdf)
[(colab)](https://colab.research.google.com/github/deepmind/open_spiel/blob/master/open_spiel/colabs/CFR_and_REINFORCE.ipynb)
If you use OpenSpiel in your research, please cite the paper using the following
BibTeX:
```bibtex
@article{LanctotEtAl2019OpenSpiel,
title = {{OpenSpiel}: A Framework for Reinforcement Learning in Games},
author = {Marc Lanctot and Edward Lockhart and Jean-Baptiste Lespiau and
Vinicius Zambaldi and Satyaki Upadhyay and Julien P\'{e}rolat and
Sriram Srinivasan and Finbarr Timbers and Karl Tuyls and
Shayegan Omidshafiei and Daniel Hennes and Dustin Morrill and
Paul Muller and Timo Ewalds and Ryan Faulkner and J\'{a}nos Kram\'{a}r
and Bart De Vylder and Brennan Saeta and James Bradbury and David Ding
and Sebastian Borgeaud and Matthew Lai and Julian Schrittwieser and
Thomas Anthony and Edward Hughes and Ivo Danihelka and Jonah Ryan-Davis},
year = {2019},
eprint = {1908.09453},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
journal = {CoRR},
volume = {abs/1908.09453},
url = {http://arxiv.org/abs/1908.09453},
}
```
## Versioning
We use [Semantic Versioning](https://semver.org/).
%package help
Summary: Development documents and examples for open-spiel
Provides: python3-open-spiel-doc
%description help
# OpenSpiel: A Framework for Reinforcement Learning in Games
[](https://openspiel.readthedocs.io/en/latest/?badge=latest)
OpenSpiel is a collection of environments and algorithms for research in general
reinforcement learning and search/planning in games. OpenSpiel supports n-player
(single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and
sequential, strictly turn-taking and simultaneous-move, perfect and imperfect
information games, as well as traditional multiagent environments such as
(partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel
also includes tools to analyze learning dynamics and other common evaluation
metrics. Games are represented as procedural extensive-form games, with some
natural extensions. The core API and games are implemented in C++ and exposed to
Python. Algorithms and tools are written both in C++ and Python.
To try OpenSpiel in Google Colaboratory, please refer to `open_spiel/colabs` subdirectory or start [here](https://colab.research.google.com/github/deepmind/open_spiel/blob/master/open_spiel/colabs/install_open_spiel.ipynb).
# Index
Please choose among the following options:
* [Installing OpenSpiel](docs/install.md)
* [Introduction to OpenSpiel](docs/intro.md)
* [API Overview and First Example](docs/concepts.md)
* [API Reference](docs/api_reference.md)
* [Overview of Implemented Games](docs/games.md)
* [Overview of Implemented Algorithms](docs/algorithms.md)
* [Developer Guide](docs/developer_guide.md)
* [Using OpenSpiel as a C++ Library](docs/library.md)
* [Guidelines and Contributing](docs/contributing.md)
* [Authors](docs/authors.md)
For a longer introduction to the core concepts, formalisms, and terminology,
including an overview of the algorithms and some results, please see
[OpenSpiel: A Framework for Reinforcement Learning in Games](https://arxiv.org/abs/1908.09453).
For an overview of OpenSpiel and example uses of the core API, please check out
our tutorials:
* [Motivation, Core API, Brief Intro to Replictor Dynamics and Imperfect
Information Games](https://www.youtube.com/watch?v=8NCPqtPwlFQ) by Marc
Lanctot.
[(slides)](http://mlanctot.info/files/OpenSpiel_Tutorial_KU_Leuven_2022.pdf)
[(colab)](https://colab.research.google.com/github/deepmind/open_spiel/blob/master/open_spiel/colabs/OpenSpielTutorial.ipynb)
* [Motivation, Core API, Implementing CFR and REINFORCE on Kuhn poker, Leduc
poker, and Goofspiel](https://www.youtube.com/watch?v=o6JNHoGUXCo) by Edward
Lockhart.
[(slides)](http://mlanctot.info/files/open_spiel_tutorial-mar2021-comarl.pdf)
[(colab)](https://colab.research.google.com/github/deepmind/open_spiel/blob/master/open_spiel/colabs/CFR_and_REINFORCE.ipynb)
If you use OpenSpiel in your research, please cite the paper using the following
BibTeX:
```bibtex
@article{LanctotEtAl2019OpenSpiel,
title = {{OpenSpiel}: A Framework for Reinforcement Learning in Games},
author = {Marc Lanctot and Edward Lockhart and Jean-Baptiste Lespiau and
Vinicius Zambaldi and Satyaki Upadhyay and Julien P\'{e}rolat and
Sriram Srinivasan and Finbarr Timbers and Karl Tuyls and
Shayegan Omidshafiei and Daniel Hennes and Dustin Morrill and
Paul Muller and Timo Ewalds and Ryan Faulkner and J\'{a}nos Kram\'{a}r
and Bart De Vylder and Brennan Saeta and James Bradbury and David Ding
and Sebastian Borgeaud and Matthew Lai and Julian Schrittwieser and
Thomas Anthony and Edward Hughes and Ivo Danihelka and Jonah Ryan-Davis},
year = {2019},
eprint = {1908.09453},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
journal = {CoRR},
volume = {abs/1908.09453},
url = {http://arxiv.org/abs/1908.09453},
}
```
## Versioning
We use [Semantic Versioning](https://semver.org/).
%prep
%autosetup -n open_spiel-1.3
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-open-spiel -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot - 1.3-1
- Package Spec generated