--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 242.61 +/- 16.03 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Diego's code ```python import gymnasium as gym from stable_baselines3.common.vec_env import DummyVecEnv from stable_baselines3.common.env_util import make_vec_env from huggingface_sb3 import package_to_hub ## TODO: Define a repo_id ## repo_id is the id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name} for instance ThomasSimonini/ppo-LunarLander-v2 repo_id = # TODO: Define the name of the environment env_id = # Create the evaluation env and set the render_mode="rgb_array" eval_env = DummyVecEnv([lambda: Monitor(gym.make(env_id, render_mode="rgb_array"))]) # TODO: Define the model architecture we used model_architecture = "" ## TODO: Define the commit message commit_message = "" # method save, evaluate, generate a model card and record a replay video of your agent before pushing the repo to the hub package_to_hub(model=model, # Our trained model model_name=model_name, # The name of our trained model model_architecture=model_architecture, # The model architecture we used: in our case PPO env_id=env_id, # Name of the environment eval_env=eval_env, # Evaluation Environment repo_id=repo_id, # id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name} for instance ThomasSimonini/ppo-LunarLander-v2 commit_message=commit_message) ... ```