Getting Started


Stable-Baselines3 (SB3) uses vectorized environments (VecEnv) internally. Please read the associated section to learn more about its features and differences compared to a single Gym environment.

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 A2C on a CartPole environment:

import gymnasium as gym

from stable_baselines3 import A2C

env = gym.make("CartPole-v1", render_mode="rgb_array")

model = A2C("MlpPolicy", env, verbose=1)

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


You can find explanations about the logger output and names in the Logger section.

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

from stable_baselines3 import A2C

model = A2C("MlpPolicy", "CartPole-v1").learn(10000)