Skip to main content
Help the Python Software Foundation raise $60,000 USD by December 31st!  Building the PSF Q4 Fundraiser

A fork of OpenAI Baselines, implementations of reinforcement learning algorithms.

Project description

Build Status Documentation Status Codacy Badge Codacy Badge

Stable Baselines

Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on 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:

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 and if the policy is registered:

from stable_baselines import PPO2

model = PPO2('MlpPolicy', 'CartPole-v1').learn(10000)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for stable-baselines, version 2.10.1
Filename, size File type Python version Upload date Hashes
Filename, size stable_baselines-2.10.1-py3-none-any.whl (240.1 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size stable_baselines-2.10.1.tar.gz (198.6 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page