%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.nju.edu.cn/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 * Mon May 15 2023 Python_Bot - 0.6.5-1 - Package Spec generated