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set_env
to keep the number of timesteps (#615)
import os
from setuptools import find_packages, setup
with open(os.path.join("stable_baselines3", "version.txt"), "r") as file_handler:
__version__ = file_handler.read().strip()
long_description = """
# Stable Baselines3
Stable Baselines3 is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of [Stable Baselines](https://github.com/hill-a/stable-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.
## Links
Repository:
https://github.com/DLR-RM/stable-baselines3
Blog post:
https://araffin.github.io/post/sb3/
Documentation:
https://stable-baselines3.readthedocs.io/en/master/
RL Baselines3 Zoo:
https://github.com/DLR-RM/rl-baselines3-zoo
SB3 Contrib:
https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
## 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 PPO on a cartpole environment:
```python
import gym
from stable_baselines3 import PPO
env = gym.make('CartPole-v1')
model = PPO('MlpPolicy', env, verbose=1)
model.learn(total_timesteps=10000)
obs = env.reset()
for i in range(1000):
action, _states = model.predict(obs, deterministic=True)
obs, reward, done, info = env.step(action)
env.render()
if done:
obs = env.reset()
```
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):
```python
from stable_baselines3 import PPO
model = PPO('MlpPolicy', 'CartPole-v1').learn(10000)
```
""" # noqa:E501
setup(
name="stable_baselines3",
packages=[package for package in find_packages() if package.startswith("stable_baselines3")],
package_data={"stable_baselines3": ["py.typed", "version.txt"]},
install_requires=[
"gym>=0.17,<0.20", # gym 0.20 breaks atari-py behavior
"numpy",
"torch>=1.8.1",
# For saving models
"cloudpickle",
# For reading logs
"pandas",
# Plotting learning curves
"matplotlib",
],
extras_require={
"tests": [
# Run tests and coverage
"pytest",
"pytest-cov",
"pytest-env",
"pytest-xdist",
# Type check
"pytype",
# Lint code
"flake8>=3.8",
# Find likely bugs
"flake8-bugbear",
# Sort imports
"isort>=5.0",
# Reformat
"black",
# For toy text Gym envs
"scipy>=1.4.1",
],
"docs": [
"sphinx",
"sphinx-autobuild",
"sphinx-rtd-theme",
# For spelling
"sphinxcontrib.spelling",
# Type hints support
"sphinx-autodoc-typehints",
],
"extra": [
# For render
"opencv-python",
# For atari games,
"atari_py~=0.2.0",
"pillow",
# Tensorboard support
"tensorboard>=2.2.0",
# Checking memory taken by replay buffer
"psutil",
],
},
description="Pytorch version of Stable Baselines, implementations of reinforcement learning algorithms.",
author="Antonin Raffin",
url="https://github.com/DLR-RM/stable-baselines3",
author_email="antonin.raffin@dlr.de",
keywords="reinforcement-learning-algorithms reinforcement-learning machine-learning "
"gym openai stable baselines toolbox python data-science",
license="MIT",
long_description=long_description,
long_description_content_type="text/markdown",
version=__version__,
python_requires=">=3.6",
# PyPI package information.
classifiers=[
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
],
)
# python setup.py sdist
# python setup.py bdist_wheel
# twine upload --repository-url https://test.pypi.org/legacy/ dist/*
# twine upload dist/*
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