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39 lines
985 B
ReStructuredText
39 lines
985 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 SAC on a Pendulum environment:
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.. code-block:: python
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import gym
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from stable_baselines3.sac.policies import MlpPolicy
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from stable_baselines3.common.vec_env import DummyVecEnv
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from stable_baselines3 import SAC
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env = gym.make('Pendulum-v0')
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model = SAC(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 = model.predict(obs)
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obs, rewards, dones, info = env.step(action)
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env.render()
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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 SAC
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model = SAC('MlpPolicy', 'Pendulum-v0').learn(10000)
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