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|
%global _empty_manifest_terminate_build 0
Name: python-stable-baselines
Version: 2.10.2
Release: 1
Summary: A fork of OpenAI Baselines, implementations of reinforcement learning algorithms.
License: MIT
URL: https://github.com/hill-a/stable-baselines
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ba/e5/b59ee753d93632fd28d15acaf5043e8cd1d14385191f0ab843f277c00a5d/stable_baselines-2.10.2.tar.gz
BuildArch: noarch
Requires: python3-gym[atari,classic_control]
Requires: python3-scipy
Requires: python3-joblib
Requires: python3-cloudpickle
Requires: python3-opencv-python
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-matplotlib
Requires: python3-sphinx
Requires: python3-sphinx-autobuild
Requires: python3-sphinx-rtd-theme
Requires: python3-mpi4py
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-pytest-env
Requires: python3-pytest-xdist
Requires: python3-pytype
%description
**WARNING: This package is in maintenance mode, please use [Stable-Baselines3 (SB3)](https://github.com/DLR-RM/stable-baselines3) for an up-to-date version. You can find a [migration guide](https://stable-baselines3.readthedocs.io/en/master/guide/migration.html) in SB3 documentation.**
[](https://travis-ci.com/hill-a/stable-baselines) [](https://stable-baselines.readthedocs.io/en/master/?badge=master) [](https://www.codacy.com/app/baselines_janitors/stable-baselines?utm_source=github.com&utm_medium=referral&utm_content=hill-a/stable-baselines&utm_campaign=Badge_Grade) [](https://www.codacy.com/app/baselines_janitors/stable-baselines?utm_source=github.com&utm_medium=referral&utm_content=hill-a/stable-baselines&utm_campaign=Badge_Coverage)
# Stable Baselines
Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI [Baselines](https://github.com/openai/baselines/).
These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. We also hope that the simplicity of these tools will allow beginners to experiment with a more advanced toolset, without being buried in implementation details.
## Main differences with OpenAI Baselines
This toolset is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups:
- Unified structure for all algorithms
- PEP8 compliant (unified code style)
- Documented functions and classes
- More tests & more code coverage
- Additional algorithms: SAC and TD3 (+ HER support for DQN, DDPG, SAC and TD3)
## Links
Repository:
https://github.com/hill-a/stable-baselines
Medium article:
https://medium.com/@araffin/df87c4b2fc82
Documentation:
https://stable-baselines.readthedocs.io/en/master/
RL Baselines Zoo:
https://github.com/araffin/rl-baselines-zoo
## Quick example
Most of the library tries to follow a sklearn-like syntax for the Reinforcement Learning algorithms using Gym.
Here is a quick example of how to train and run PPO2 on a cartpole environment:
```python
import gym
from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import PPO2
env = gym.make('CartPole-v1')
# Optional: PPO2 requires a vectorized environment to run
# the env is now wrapped automatically when passing it to the constructor
# env = DummyVecEnv([lambda: env])
model = PPO2(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=10000)
obs = env.reset()
for i in range(1000):
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
```
Or just train a model with a one liner if [the environment is registered in Gym](https://github.com/openai/gym/wiki/Environments) and if [the policy is registered](https://stable-baselines.readthedocs.io/en/master/guide/custom_policy.html):
```python
from stable_baselines import PPO2
model = PPO2('MlpPolicy', 'CartPole-v1').learn(10000)
```
%package -n python3-stable-baselines
Summary: A fork of OpenAI Baselines, implementations of reinforcement learning algorithms.
Provides: python-stable-baselines
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-stable-baselines
**WARNING: This package is in maintenance mode, please use [Stable-Baselines3 (SB3)](https://github.com/DLR-RM/stable-baselines3) for an up-to-date version. You can find a [migration guide](https://stable-baselines3.readthedocs.io/en/master/guide/migration.html) in SB3 documentation.**
[](https://travis-ci.com/hill-a/stable-baselines) [](https://stable-baselines.readthedocs.io/en/master/?badge=master) [](https://www.codacy.com/app/baselines_janitors/stable-baselines?utm_source=github.com&utm_medium=referral&utm_content=hill-a/stable-baselines&utm_campaign=Badge_Grade) [](https://www.codacy.com/app/baselines_janitors/stable-baselines?utm_source=github.com&utm_medium=referral&utm_content=hill-a/stable-baselines&utm_campaign=Badge_Coverage)
# Stable Baselines
Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI [Baselines](https://github.com/openai/baselines/).
These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. We also hope that the simplicity of these tools will allow beginners to experiment with a more advanced toolset, without being buried in implementation details.
## Main differences with OpenAI Baselines
This toolset is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups:
- Unified structure for all algorithms
- PEP8 compliant (unified code style)
- Documented functions and classes
- More tests & more code coverage
- Additional algorithms: SAC and TD3 (+ HER support for DQN, DDPG, SAC and TD3)
## Links
Repository:
https://github.com/hill-a/stable-baselines
Medium article:
https://medium.com/@araffin/df87c4b2fc82
Documentation:
https://stable-baselines.readthedocs.io/en/master/
RL Baselines Zoo:
https://github.com/araffin/rl-baselines-zoo
## Quick example
Most of the library tries to follow a sklearn-like syntax for the Reinforcement Learning algorithms using Gym.
Here is a quick example of how to train and run PPO2 on a cartpole environment:
```python
import gym
from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import PPO2
env = gym.make('CartPole-v1')
# Optional: PPO2 requires a vectorized environment to run
# the env is now wrapped automatically when passing it to the constructor
# env = DummyVecEnv([lambda: env])
model = PPO2(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=10000)
obs = env.reset()
for i in range(1000):
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
```
Or just train a model with a one liner if [the environment is registered in Gym](https://github.com/openai/gym/wiki/Environments) and if [the policy is registered](https://stable-baselines.readthedocs.io/en/master/guide/custom_policy.html):
```python
from stable_baselines import PPO2
model = PPO2('MlpPolicy', 'CartPole-v1').learn(10000)
```
%package help
Summary: Development documents and examples for stable-baselines
Provides: python3-stable-baselines-doc
%description help
**WARNING: This package is in maintenance mode, please use [Stable-Baselines3 (SB3)](https://github.com/DLR-RM/stable-baselines3) for an up-to-date version. You can find a [migration guide](https://stable-baselines3.readthedocs.io/en/master/guide/migration.html) in SB3 documentation.**
[](https://travis-ci.com/hill-a/stable-baselines) [](https://stable-baselines.readthedocs.io/en/master/?badge=master) [](https://www.codacy.com/app/baselines_janitors/stable-baselines?utm_source=github.com&utm_medium=referral&utm_content=hill-a/stable-baselines&utm_campaign=Badge_Grade) [](https://www.codacy.com/app/baselines_janitors/stable-baselines?utm_source=github.com&utm_medium=referral&utm_content=hill-a/stable-baselines&utm_campaign=Badge_Coverage)
# Stable Baselines
Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI [Baselines](https://github.com/openai/baselines/).
These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. We also hope that the simplicity of these tools will allow beginners to experiment with a more advanced toolset, without being buried in implementation details.
## Main differences with OpenAI Baselines
This toolset is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups:
- Unified structure for all algorithms
- PEP8 compliant (unified code style)
- Documented functions and classes
- More tests & more code coverage
- Additional algorithms: SAC and TD3 (+ HER support for DQN, DDPG, SAC and TD3)
## Links
Repository:
https://github.com/hill-a/stable-baselines
Medium article:
https://medium.com/@araffin/df87c4b2fc82
Documentation:
https://stable-baselines.readthedocs.io/en/master/
RL Baselines Zoo:
https://github.com/araffin/rl-baselines-zoo
## Quick example
Most of the library tries to follow a sklearn-like syntax for the Reinforcement Learning algorithms using Gym.
Here is a quick example of how to train and run PPO2 on a cartpole environment:
```python
import gym
from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import PPO2
env = gym.make('CartPole-v1')
# Optional: PPO2 requires a vectorized environment to run
# the env is now wrapped automatically when passing it to the constructor
# env = DummyVecEnv([lambda: env])
model = PPO2(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=10000)
obs = env.reset()
for i in range(1000):
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
```
Or just train a model with a one liner if [the environment is registered in Gym](https://github.com/openai/gym/wiki/Environments) and if [the policy is registered](https://stable-baselines.readthedocs.io/en/master/guide/custom_policy.html):
```python
from stable_baselines import PPO2
model = PPO2('MlpPolicy', 'CartPole-v1').learn(10000)
```
%prep
%autosetup -n stable-baselines-2.10.2
%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-stable-baselines -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 2.10.2-1
- Package Spec generated
|
