mirror of
https://github.com/saymrwulf/stable-baselines3.git
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2c924f52f5
* Change import * Update custom policy doc * Re-enable sphinx_autodoc_typehints * Update docker image * Attempt to fix read the doc build error * Add sphinx_autodoc_typehints to read the doc env * Fix pip version * Add full custom policy example * Fix
135 lines
4.1 KiB
Python
135 lines
4.1 KiB
Python
import os
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from setuptools import find_packages, setup
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with open(os.path.join("stable_baselines3", "version.txt"), "r") as file_handler:
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__version__ = file_handler.read().strip()
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long_description = """
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# Stable Baselines3
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Stable Baselines3 is a set of improved implementations of reinforcement learning algorithms in PyTorch. It is the next major version of [Stable Baselines](https://github.com/hill-a/stable-baselines).
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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.
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## Links
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Repository:
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https://github.com/DLR-RM/stable-baselines3
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Medium article:
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https://medium.com/@araffin/df87c4b2fc82
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Documentation:
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https://stable-baselines3.readthedocs.io/en/master/
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RL Baselines3 Zoo:
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https://github.com/DLR-RM/rl-baselines3-zoo
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## Quick example
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Most of the library tries to follow a sklearn-like syntax for the Reinforcement Learning algorithms using Gym.
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Here is a quick example of how to train and run PPO on a cartpole environment:
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```python
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import gym
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from stable_baselines3 import PPO
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env = gym.make('CartPole-v1')
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model = PPO('MlpPolicy', env, verbose=1)
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model.learn(total_timesteps=10000)
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obs = env.reset()
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for i in range(1000):
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action, _states = model.predict(obs, deterministic=True)
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obs, reward, done, info = env.step(action)
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env.render()
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if done:
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obs = env.reset()
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```
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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-baselines3.readthedocs.io/en/master/guide/custom_policy.html):
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```python
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from stable_baselines3 import PPO
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model = PPO('MlpPolicy', 'CartPole-v1').learn(10000)
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```
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""" # noqa:E501
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setup(
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name="stable_baselines3",
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packages=[package for package in find_packages() if package.startswith("stable_baselines3")],
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package_data={"stable_baselines3": ["py.typed", "version.txt"]},
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install_requires=[
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"gym>=0.17",
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"numpy",
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"torch>=1.4.0",
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# For saving models
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"cloudpickle",
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# For reading logs
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"pandas",
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# Plotting learning curves
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"matplotlib",
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],
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extras_require={
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"tests": [
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# Run tests and coverage
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"pytest",
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"pytest-cov",
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"pytest-env",
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"pytest-xdist",
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# Type check
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"pytype",
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# Lint code
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"flake8>=3.8",
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# Sort imports
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"isort>=5.0",
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# Reformat
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"black",
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],
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"docs": [
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"sphinx",
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"sphinx-autobuild",
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"sphinx-rtd-theme",
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# For spelling
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"sphinxcontrib.spelling",
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# Type hints support
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"sphinx-autodoc-typehints",
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],
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"extra": [
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# For render
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"opencv-python",
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# For atari games,
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"atari_py~=0.2.0",
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"pillow",
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# Tensorboard support
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"tensorboard",
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# Checking memory taken by replay buffer
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"psutil",
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],
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},
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description="Pytorch version of Stable Baselines, implementations of reinforcement learning algorithms.",
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author="Antonin Raffin",
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url="https://github.com/DLR-RM/stable-baselines3",
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author_email="antonin.raffin@dlr.de",
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keywords="reinforcement-learning-algorithms reinforcement-learning machine-learning "
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"gym openai stable baselines toolbox python data-science",
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license="MIT",
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long_description=long_description,
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long_description_content_type="text/markdown",
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version=__version__,
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)
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# python setup.py sdist
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# python setup.py bdist_wheel
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# twine upload --repository-url https://test.pypi.org/legacy/ dist/*
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# twine upload dist/*
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