| import gym | |
| from stable_baselines3 import A2C | |
| from stable_baselines3.common.env_util import make_vec_env | |
| # Parallel environments | |
| env = gym.make("CartPole-v1") | |
| model = A2C("MlpPolicy", env, verbose=1) | |
| model.learn(total_timesteps=25000) | |
| obs = env.reset() | |
| for i in range (10000): | |
| action, _states = model.predict(obs) | |
| obs, rewards, dones, info = env.step(action) | |
| env.render() | |
| env.close() | |
| model.save("a2c_Cart_Pole") | |