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40 lines
984 B
ReStructuredText
40 lines
984 B
ReStructuredText
.. _quickstart:
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===============
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Getting Started
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===============
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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 gym
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from stable_baselines3 import A2C
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from stable_baselines3.a2c import MlpPolicy
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env = gym.make('CartPole-v1')
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model = A2C(MlpPolicy, env, verbose=1)
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model.learn(total_timesteps=10000)
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obs = 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 = env.step(action)
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env.render()
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if done:
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obs = env.reset()
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Or just train a model with a one liner if
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`the environment is registered in Gym <https://github.com/openai/gym/wiki/Environments>`_ 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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