(quickstart)=

# Getting Started

:::{note}
Stable-Baselines3 (SB3) uses [vectorized environments (VecEnv)](vec_envs.md) internally.
Please read the associated section to learn more about its features and differences compared to a single Gym environment.
:::

Most of the library follows 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:

```python
import gymnasium as gym

from stable_baselines3 import A2C

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

model = A2C("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, _state = model.predict(obs, deterministic=True)
    obs, reward, done, info = vec_env.step(action)
    vec_env.render("human")
    # VecEnv resets automatically
    # if done:
    #   obs = vec_env.reset()
```

:::{note}
You can find explanations about the logger output and names in the {ref}`Logger <logger>` section.
:::

Or just train a model with a one-liner if
[the environment is registered in Gymnasium](https://gymnasium.farama.org/tutorials/gymnasium_basics/environment_creation/#registering-envs) and the
policy is registered:

```python
from stable_baselines3 import A2C

model = A2C("MlpPolicy", "CartPole-v1").learn(10_000)
```