automatic import of torchrl

1 files changed, 58 insertions, 58 deletions

diff --git a/torchrl.spec b/torchrl.spec
index db67173..cdcc713 100644
--- a/torchrl.spec
+++ b/torchrl.spec
@@ -1,59 +1,59 @@
-%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

-

-%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 

+%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
+
+%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