Getting StartedΒΆ
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=10000)
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()
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)