import gym from stable_baselines import PPO2 # Create the environment env = gym.make('CartPole-v0')
# Train a model model = PPO2('MlpPolicy', env, verbose=1) model.learn(total_timesteps=20000) # Test the trained model on the environment obs = env.reset() while True: action, _states = model.predict(obs) obs, rewards, dones, info = env.step(action) env.render() if dones: obs = env.reset()
model = PPO2('MlpPolicy', env, gamma=0.99, learning_rate=0.001, n_steps=2048, nminibatches=32, verbose=1)Overall, PPO2 is a reliable reinforcement learning algorithm provided by the Stable Baselines package that can be used to train agents to perform various tasks in different environments.