Stable Baselines3
Stable Baselines3 (SB3) 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.
Note: despite its simplicity of use, Stable Baselines3 (SB3) assumes you have some knowledge about Reinforcement Learning (RL). You should not utilize this library without some practice. To that extent, we provide good resources in the documentation to get started with RL.
Installation
Note: Stable-Baselines3 supports PyTorch >= 1.8.1.
Prerequisites
Stable Baselines3 requires python 3.6+.
Windows 10
To install stable-baselines on Windows, please look at the documentation.
Install using pip
Install the Stable Baselines3 package:
pip install stable-baselines3[extra]
Note: Some shells such as Zsh require quotation marks around brackets, i.e. pip install 'stable-baselines3[extra]' (More Info).
This includes an optional dependencies like Tensorboard, OpenCV or atari-py to train on atari games. If you do not need those, you can use:
pip install stable-baselines3
Please read the documentation for more details and alternatives (from source, using docker).
Example
Most of the library tries to follow a sklearn-like syntax for the Reinforcement Learning algorithms.
Here is a quick example of how to train and run PPO on a cartpole environment:
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()
env.close()
Or just train a model with a one liner if the environment is registered in Gym and if the policy is registered:
from stable_baselines3 import PPO
model = PPO('MlpPolicy', 'CartPole-v1').learn(10000)
Please read the documentation for more examples.
