%global _empty_manifest_terminate_build 0 Name: python-pytorchrl Version: 3.2.11 Release: 1 Summary: Disributed RL implementations with ray and pytorch. License: MIT License URL: https://github.com/PyTorchRL/pytorchrl/ Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b8/b0/dbd0fba3ddfa2b5cd8818e85497478b857f26a843aeba5cb35aa88cc2590/pytorchrl-3.2.11.tar.gz BuildArch: noarch Requires: python3-gym[atari] Requires: python3-gym[accept-rom-license] Requires: python3-ray[default] Requires: python3-numpy Requires: python3-pandas Requires: python3-scipy Requires: python3-lz4 Requires: python3-tqdm Requires: python3-opencv-python Requires: python3-wandb Requires: python3-hydra-core %description ## PyTorchRL: A PyTorch library for reinforcement learning Deep Reinforcement learning (DRL) has been very successful in recent years but current methods still require vast amounts of data to solve non-trivial environments. Scaling to solve more complex tasks requires frameworks that are flexible enough to allow prototyping and testing of new ideas, yet avoiding the impractically slow experimental turnaround times associated to single-threaded implementations. PyTorchRL is a pytorch-based library for DRL that allows to easily assemble RL agents using a set of core reusable and easily extendable sub-modules as building blocks. To reduce training times, PyTorchRL allows scaling agents with a parameterizable component called Scheme, that permits to define distributed architectures with great flexibility by specifying which operations should be decoupled, which should be parallelized, and how parallel tasks should be synchronized. ### Installation ``` conda create -y -n pytorchrl conda activate pytorchrl conda install pytorch torchvision cudatoolkit -c pytorch pip install pytorchrl ``` ### Documentation PyTorchRL documentation can be found [here](https://pytorchrl.readthedocs.io/en/latest/). ### Citing PyTorchRL Here is the [paper](https://arxiv.org/abs/2007.02622) ``` @misc{bou2021pytorchrl, title={PyTorchRL: Modular and Distributed Reinforcement Learning in PyTorch}, author={Albert Bou and Gianni De Fabritiis}, year={2021}, eprint={2007.02622}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` %package -n python3-pytorchrl Summary: Disributed RL implementations with ray and pytorch. Provides: python-pytorchrl BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pytorchrl ## PyTorchRL: A PyTorch library for reinforcement learning Deep Reinforcement learning (DRL) has been very successful in recent years but current methods still require vast amounts of data to solve non-trivial environments. Scaling to solve more complex tasks requires frameworks that are flexible enough to allow prototyping and testing of new ideas, yet avoiding the impractically slow experimental turnaround times associated to single-threaded implementations. PyTorchRL is a pytorch-based library for DRL that allows to easily assemble RL agents using a set of core reusable and easily extendable sub-modules as building blocks. To reduce training times, PyTorchRL allows scaling agents with a parameterizable component called Scheme, that permits to define distributed architectures with great flexibility by specifying which operations should be decoupled, which should be parallelized, and how parallel tasks should be synchronized. ### Installation ``` conda create -y -n pytorchrl conda activate pytorchrl conda install pytorch torchvision cudatoolkit -c pytorch pip install pytorchrl ``` ### Documentation PyTorchRL documentation can be found [here](https://pytorchrl.readthedocs.io/en/latest/). ### Citing PyTorchRL Here is the [paper](https://arxiv.org/abs/2007.02622) ``` @misc{bou2021pytorchrl, title={PyTorchRL: Modular and Distributed Reinforcement Learning in PyTorch}, author={Albert Bou and Gianni De Fabritiis}, year={2021}, eprint={2007.02622}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` %package help Summary: Development documents and examples for pytorchrl Provides: python3-pytorchrl-doc %description help ## PyTorchRL: A PyTorch library for reinforcement learning Deep Reinforcement learning (DRL) has been very successful in recent years but current methods still require vast amounts of data to solve non-trivial environments. Scaling to solve more complex tasks requires frameworks that are flexible enough to allow prototyping and testing of new ideas, yet avoiding the impractically slow experimental turnaround times associated to single-threaded implementations. PyTorchRL is a pytorch-based library for DRL that allows to easily assemble RL agents using a set of core reusable and easily extendable sub-modules as building blocks. To reduce training times, PyTorchRL allows scaling agents with a parameterizable component called Scheme, that permits to define distributed architectures with great flexibility by specifying which operations should be decoupled, which should be parallelized, and how parallel tasks should be synchronized. ### Installation ``` conda create -y -n pytorchrl conda activate pytorchrl conda install pytorch torchvision cudatoolkit -c pytorch pip install pytorchrl ``` ### Documentation PyTorchRL documentation can be found [here](https://pytorchrl.readthedocs.io/en/latest/). ### Citing PyTorchRL Here is the [paper](https://arxiv.org/abs/2007.02622) ``` @misc{bou2021pytorchrl, title={PyTorchRL: Modular and Distributed Reinforcement Learning in PyTorch}, author={Albert Bou and Gianni De Fabritiis}, year={2021}, eprint={2007.02622}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` %prep %autosetup -n pytorchrl-3.2.11 %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-pytorchrl -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 3.2.11-1 - Package Spec generated