| import gym | |
| from stable_baselines3 import A2C | |
| # Set up the CartPole environement | |
| # Create the environment | |
| env = gym.make("CartPole-v1") | |
| # Reset the environment and get the initial observation | |
| observation = env.reset() | |
| print('observation space:', env.observation_space) | |
| print('action space:', env.action_space) | |
| print('threshold: ', env.spec.reward_threshold) | |
| model = A2C("MlpPolicy", env, verbose=1) | |
| print(model) | |
| model.learn(total_timesteps=25000) | |
| for _ in range(500): | |
| observation = env.reset() | |
| done = False | |
| while not done: | |
| action, _observations = model.predict(observation) | |
| observation, reward, done, info = env.step(action) | |
| env.render() |