%global _empty_manifest_terminate_build 0 Name: python-SuperSuit Version: 3.7.2 Release: 1 Summary: Wrappers for Gymnasium and PettingZoo License: MIT License URL: https://github.com/Farama-Foundation/SuperSuit Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ea/46/a966520971d4dc1c5159b13d4cfb0b7e15ebf12e5a8aafd98d807ee2c93d/SuperSuit-3.7.2.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-gymnasium %description

SuperSuit introduces a collection of small functions which can wrap reinforcement learning environments to do preprocessing ('microwrappers'). We support Gymnasium for single agent environments and PettingZoo for multi-agent environments (both AECEnv and ParallelEnv environments). Using it with Gymnasium to convert space invaders to have a grey scale observation space and stack the last 4 frames looks like: ``` import gymnasium from supersuit import color_reduction_v0, frame_stack_v1 env = gymnasium.make('SpaceInvaders-v0') env = frame_stack_v1(color_reduction_v0(env, 'full'), 4) ``` Similarly, using SuperSuit with PettingZoo environments looks like ``` from pettingzoo.butterfly import pistonball_v0 env = pistonball_v0.env() env = frame_stack_v1(color_reduction_v0(env, 'full'), 4) ``` **Please note**: Once the planned wrapper rewrite of Gymnasium is complete and the vector API is stabilized, this project will be deprecated and rewritten as part of a new wrappers package in PettingZoo and the vectorized API will be redone, taking inspiration from the functionality currently in Gymnasium. ## Installing SuperSuit To install SuperSuit from pypi: ``` python3 -m venv env source env/bin/activate pip install --upgrade pip pip install supersuit ``` Alternatively, to install SuperSuit from source, clone this repo, `cd` to it, and then: ``` python3 -m venv env source env/bin/activate pip install --upgrade pip pip install -e . ``` %package -n python3-SuperSuit Summary: Wrappers for Gymnasium and PettingZoo Provides: python-SuperSuit BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-SuperSuit

SuperSuit introduces a collection of small functions which can wrap reinforcement learning environments to do preprocessing ('microwrappers'). We support Gymnasium for single agent environments and PettingZoo for multi-agent environments (both AECEnv and ParallelEnv environments). Using it with Gymnasium to convert space invaders to have a grey scale observation space and stack the last 4 frames looks like: ``` import gymnasium from supersuit import color_reduction_v0, frame_stack_v1 env = gymnasium.make('SpaceInvaders-v0') env = frame_stack_v1(color_reduction_v0(env, 'full'), 4) ``` Similarly, using SuperSuit with PettingZoo environments looks like ``` from pettingzoo.butterfly import pistonball_v0 env = pistonball_v0.env() env = frame_stack_v1(color_reduction_v0(env, 'full'), 4) ``` **Please note**: Once the planned wrapper rewrite of Gymnasium is complete and the vector API is stabilized, this project will be deprecated and rewritten as part of a new wrappers package in PettingZoo and the vectorized API will be redone, taking inspiration from the functionality currently in Gymnasium. ## Installing SuperSuit To install SuperSuit from pypi: ``` python3 -m venv env source env/bin/activate pip install --upgrade pip pip install supersuit ``` Alternatively, to install SuperSuit from source, clone this repo, `cd` to it, and then: ``` python3 -m venv env source env/bin/activate pip install --upgrade pip pip install -e . ``` %package help Summary: Development documents and examples for SuperSuit Provides: python3-SuperSuit-doc %description help

SuperSuit introduces a collection of small functions which can wrap reinforcement learning environments to do preprocessing ('microwrappers'). We support Gymnasium for single agent environments and PettingZoo for multi-agent environments (both AECEnv and ParallelEnv environments). Using it with Gymnasium to convert space invaders to have a grey scale observation space and stack the last 4 frames looks like: ``` import gymnasium from supersuit import color_reduction_v0, frame_stack_v1 env = gymnasium.make('SpaceInvaders-v0') env = frame_stack_v1(color_reduction_v0(env, 'full'), 4) ``` Similarly, using SuperSuit with PettingZoo environments looks like ``` from pettingzoo.butterfly import pistonball_v0 env = pistonball_v0.env() env = frame_stack_v1(color_reduction_v0(env, 'full'), 4) ``` **Please note**: Once the planned wrapper rewrite of Gymnasium is complete and the vector API is stabilized, this project will be deprecated and rewritten as part of a new wrappers package in PettingZoo and the vectorized API will be redone, taking inspiration from the functionality currently in Gymnasium. ## Installing SuperSuit To install SuperSuit from pypi: ``` python3 -m venv env source env/bin/activate pip install --upgrade pip pip install supersuit ``` Alternatively, to install SuperSuit from source, clone this repo, `cd` to it, and then: ``` python3 -m venv env source env/bin/activate pip install --upgrade pip pip install -e . ``` %prep %autosetup -n SuperSuit-3.7.2 %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-SuperSuit -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon Apr 10 2023 Python_Bot - 3.7.2-1 - Package Spec generated