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 gym from stable_baselines3 import A2C env = gym.make("CartPole-v1") model = A2C("MlpPolicy", env, verbose=1) model.learn(total_timesteps=10_000) obs = env.reset() for i in range(1000): action, _state = model.predict(obs, deterministic=True) obs, reward, done, info = env.step(action) env.render() if done: obs = env.reset()
You can find explanations about the logger output and names in the Logger section.
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 A2C model = A2C("MlpPolicy", "CartPole-v1").learn(10000)