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%global _empty_manifest_terminate_build 0
Name: torchrl
Version: 0.3.1
Release: 1
Summary: TorchRL is a library of reusable components for deep learning with reinforcement learning
License: MIT
URL: https://torchrl.org/
Source0: https://github.com/pytorch/rl/archive/refs/tags/v%{version}.tar.gz/rl-%{version}.tar.gz
BuildRequires: g++
Requires: python3-torch
Requires: python3-gym
%description
TorchRL provides efficient, reusable components for Reinforcement Learning research with PyTorch.
Key features include:
- Data structures for storing and manipulating reinforcement learning environments
- Efficient operations on these structures (sampling, loss functions)
- A framework for policy gradient methods
- A framework for Q-learning methods.
%package -n python3-torchrl
Summary: TorchRL is a library of reusable components for deep learning with reinforcement learning
Provides: python-torchrl
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-setuptools_scm
BuildRequires: python3-pbr
BuildRequires: python3-pip
BuildRequires: python3-wheel
BuildRequires: python3-pytorch
BuildRequires: ninja-build
%description -n python3-torchrl
TorchRL provides efficient, reusable components for Reinforcement Learning research with PyTorch.
Key features include:
- Data structures for storing and manipulating reinforcement learning environments
- Efficient operations on these structures (sampling, loss functions)
- A framework for policy gradient methods
- A framework for Q-learning methods.
%prep
%autosetup -p1 -n rl-%{version}
%build
%py3_build
%install
%py3_install
%files -n python3-torchrl
%doc *.md
%license LICENSE
%{_bindir}/torchrl_runner
%{_bindir}/torchrl_visualizer
%{python3_sitearch}/*
%changelog
* Mon Apr 15 2024 weilaijishu
- Initial package
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