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%global _empty_manifest_terminate_build 0
Name: python-Tensorforce
Version: 0.6.5
Release: 1
Summary: Tensorforce: a TensorFlow library for applied reinforcement learning
License: Apache 2.0
URL: http://github.com/tensorforce/tensorforce
Source0: https://mirrors.aliyun.com/pypi/web/packages/cd/f1/2da8d2547dc1f6f3eb04f42472bc9126c50c4e2527c784b5d52e8ae0219f/Tensorforce-0.6.5.tar.gz
BuildArch: noarch
Requires: python3-gym
Requires: python3-h5py
Requires: python3-matplotlib
Requires: python3-msgpack
Requires: python3-msgpack-numpy
Requires: python3-numpy
Requires: python3-Pillow
Requires: python3-tensorflow
Requires: python3-tqdm
Requires: python3-ale-py
Requires: python3-pygame
Requires: python3-opencv-python
Requires: python3-m2r
Requires: python3-recommonmark
Requires: python3-sphinx
Requires: python3-sphinx-rtd-theme
Requires: python3-ale-py
Requires: python3-gym[atari,box2d,classic_control]
Requires: python3-box2d
Requires: python3-gym-retro
Requires: python3-vizdoom
Requires: python3-gym[box2d,classic_control]
Requires: python3-box2d
Requires: python3-gym-retro
Requires: python3-pytest
Requires: python3-tensorflow-addons
Requires: python3-hpbandster
Requires: python3-vizdoom
%description
# Tensorforce: a TensorFlow library for applied reinforcement learning
Tensorforce is an open-source deep reinforcement learning framework, with an emphasis on modularized flexible library design and straightforward usability for applications in research and practice. Tensorforce is built on top of [Google's TensorFlow framework](https://www.tensorflow.org/) and requires Python 3.
Tensorforce follows a set of high-level design choices which differentiate it from other similar libraries:
- **Modular component-based design**: Feature implementations, above all, strive to be as generally applicable and configurable as possible, potentially at some cost of faithfully resembling details of the introducing paper.
- **Separation of RL algorithm and application**: Algorithms are agnostic to the type and structure of inputs (states/observations) and outputs (actions/decisions), as well as the interaction with the application environment.
- **Full-on TensorFlow models**: The entire reinforcement learning logic, including control flow, is implemented in TensorFlow, to enable portable computation graphs independent of application programming language, and to facilitate the deployment of models.
For more information, see the [GitHub project page](https://github.com/tensorforce/tensorforce) and [ReadTheDocs documentation](https://tensorforce.readthedocs.io/en/latest/).
%package -n python3-Tensorforce
Summary: Tensorforce: a TensorFlow library for applied reinforcement learning
Provides: python-Tensorforce
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-Tensorforce
# Tensorforce: a TensorFlow library for applied reinforcement learning
Tensorforce is an open-source deep reinforcement learning framework, with an emphasis on modularized flexible library design and straightforward usability for applications in research and practice. Tensorforce is built on top of [Google's TensorFlow framework](https://www.tensorflow.org/) and requires Python 3.
Tensorforce follows a set of high-level design choices which differentiate it from other similar libraries:
- **Modular component-based design**: Feature implementations, above all, strive to be as generally applicable and configurable as possible, potentially at some cost of faithfully resembling details of the introducing paper.
- **Separation of RL algorithm and application**: Algorithms are agnostic to the type and structure of inputs (states/observations) and outputs (actions/decisions), as well as the interaction with the application environment.
- **Full-on TensorFlow models**: The entire reinforcement learning logic, including control flow, is implemented in TensorFlow, to enable portable computation graphs independent of application programming language, and to facilitate the deployment of models.
For more information, see the [GitHub project page](https://github.com/tensorforce/tensorforce) and [ReadTheDocs documentation](https://tensorforce.readthedocs.io/en/latest/).
%package help
Summary: Development documents and examples for Tensorforce
Provides: python3-Tensorforce-doc
%description help
# Tensorforce: a TensorFlow library for applied reinforcement learning
Tensorforce is an open-source deep reinforcement learning framework, with an emphasis on modularized flexible library design and straightforward usability for applications in research and practice. Tensorforce is built on top of [Google's TensorFlow framework](https://www.tensorflow.org/) and requires Python 3.
Tensorforce follows a set of high-level design choices which differentiate it from other similar libraries:
- **Modular component-based design**: Feature implementations, above all, strive to be as generally applicable and configurable as possible, potentially at some cost of faithfully resembling details of the introducing paper.
- **Separation of RL algorithm and application**: Algorithms are agnostic to the type and structure of inputs (states/observations) and outputs (actions/decisions), as well as the interaction with the application environment.
- **Full-on TensorFlow models**: The entire reinforcement learning logic, including control flow, is implemented in TensorFlow, to enable portable computation graphs independent of application programming language, and to facilitate the deployment of models.
For more information, see the [GitHub project page](https://github.com/tensorforce/tensorforce) and [ReadTheDocs documentation](https://tensorforce.readthedocs.io/en/latest/).
%prep
%autosetup -n Tensorforce-0.6.5
%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-Tensorforce -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.5-1
- Package Spec generated
|
