stable-baselines3

v2.3.2

Pytorch version of Stable Baselines, implementations of reinforcement learning algorithms. For more information about how to use this package see README

Latest version published 16 days ago

License: MIT

PyPI

GitHub

Package Health Score

93 / 100

security
No known security issues
popularity
Popular
maintenance
Healthy
community
Active

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Popularity

Popular

GitHub Stars: 7.99K
Forks: 1.56K
Contributors: 140

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package stable-baselines3, we found that it has been starred 7,994 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Apr 27, 2024
Direct Vulnerabilities: 0
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Indirect Vulnerabilities: 0
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License Risk: 0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

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Maintenance

Healthy

Commit Frequency

Open Issues: 70
Open PR: 19
Last Release: 16 days ago
Last Commit: 16 days ago

Further analysis of the maintenance status of stable-baselines3 based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Healthy.

We found that stable-baselines3 demonstrates a positive version release cadence with at least one new version released in the past 3 months.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 140
Funding: No

A good and healthy external contribution signal for stable-baselines3 project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: >=3.8

Age: 4 years
Latest Release: 16 days ago
Dependencies: 3 Direct
Versions: 87
Maintainers: 5
Wheels: OS Independent

Readme

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.

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:

import gymnasium

from stable_baselines3 import PPO

env = gymnasium.make("CartPole-v1", render_mode="human")

model = PPO("MlpPolicy", env, verbose=1)
model.learn(total_timesteps=10_000)

vec_env = model.get_env()
obs = vec_env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = vec_env.step(action)
    vec_env.render()
    # VecEnv resets automatically
    # if done:
    #   obs = vec_env.reset()

Or just train a model with a one liner if the environment is registered in Gymnasium and if the policy is registered:

from stable_baselines3 import PPO

model = PPO("MlpPolicy", "CartPole-v1").learn(10_000)

stable-baselines3 dependencies

cloudpickle matplotlib pandas

FAQs

What is stable-baselines3?

Pytorch version of Stable Baselines, implementations of reinforcement learning algorithms. Visit Snyk Advisor to see a full health score report for stable-baselines3, including popularity, security, maintenance & community analysis.

Is stable-baselines3 popular?

The python package stable-baselines3 receives a total of 41,866 weekly downloads. As such, stable-baselines3 popularity was classified as a popular. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is stable-baselines3 well maintained?

We found that stable-baselines3 demonstrated a healthy version release cadence and project activity. It has a community of 140 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is stable-baselines3 safe to use?

The python package stable-baselines3 was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 12 May-2024, at 08:55 (UTC).

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