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fd0cd82339
* Update outdated custom env doc * fix render_mode and term/trunc/reset_info * gym -> gymnasium --------- Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
51 lines
1.4 KiB
ReStructuredText
51 lines
1.4 KiB
ReStructuredText
.. _quickstart:
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===============
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Getting Started
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===============
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.. note::
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Stable-Baselines3 (SB3) uses :ref:`vectorized environments (VecEnv) <vec_env>` internally.
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Please read the associated section to learn more about its features and differences compared to a single Gym environment.
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Most of the library tries to follow a sklearn-like syntax for the Reinforcement Learning algorithms.
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Here is a quick example of how to train and run A2C on a CartPole environment:
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.. code-block:: python
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import gymnasium as gym
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from stable_baselines3 import A2C
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env = gym.make("CartPole-v1", render_mode="rgb_array")
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model = A2C("MlpPolicy", env, verbose=1)
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model.learn(total_timesteps=10_000)
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vec_env = model.get_env()
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obs = vec_env.reset()
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for i in range(1000):
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action, _state = model.predict(obs, deterministic=True)
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obs, reward, done, info = vec_env.step(action)
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vec_env.render("human")
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# VecEnv resets automatically
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# if done:
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# obs = vec_env.reset()
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.. note::
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You can find explanations about the logger output and names in the :ref:`Logger <logger>` section.
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Or just train a model with a one line if
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`the environment is registered in Gymnasium <https://gymnasium.farama.org/tutorials/gymnasium_basics/environment_creation/#registering-envs>`_ and if
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the policy is registered:
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.. code-block:: python
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from stable_baselines3 import A2C
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model = A2C("MlpPolicy", "CartPole-v1").learn(10000)
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