| #@title | |
| --- | |
| tags: | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| - stable-baselines3 | |
| --- | |
| # PPO LunarLander-v2 🚀🌑 | |
| This is a pre-trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library. | |
| ### Usage (with Stable-baselines3) | |
| Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed: | |
| ``` | |
| pip install stable-baselines3 | |
| pip install huggingface_sb3 | |
| ``` | |
| Then, you can use the model like this: | |
| ```python | |
| import gym | |
| from huggingface_sb3 import load_from_hub | |
| from stable_baselines3 import PPO | |
| from stable_baselines3.common.evaluation import evaluate_policy | |
| # Retrieve the model from the hub | |
| ## repo_id = id of the model repository from the Hugging Face Hub (repo_id = mrm8488/ppo-LunarLander-v2) | |
| ## filename = name of the model zip file from the repository | |
| checkpoint = load_from_hub(repo_id="mrm8488/ppo-LunarLander-v2", filename="ppo-LunarLander-v2") | |
| model = PPO.load(checkpoint) | |
| # Evaluate the agent | |
| eval_env = gym.make('LunarLander-v2') | |
| mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) | |
| print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") | |
| # Watch the agent play | |
| obs = env.reset() | |
| for i in range(1000): | |
| action, _state = model.predict(obs) | |
| obs, reward, done, info = env.step(action) | |
| env.render() | |
| if done: | |
| obs = env.reset() | |
| env.close() | |
| ``` | |
| ### Evaluation Results | |
| Mean_reward: 254.72 +/- 21.70 | |