Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Laz4rz/hf-LunarLander-1-ppo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Laz4rz/hf-LunarLander-1-ppo with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Laz4rz/hf-LunarLander-1-ppo", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
Usage (with Stable-baselines3)
Follow to eval the agent locally:
repo_id = "Laz4rz/hf-LunarLander-1-ppo" # The repo_id
filename = "ppo-LunarLander-v2.zip" # The model filename.zip
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint)
eval_env = Monitor(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}")
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Evaluation results
- mean_reward on LunarLander-v2self-reported261.43 +/- 17.17
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Laz4rz/hf-LunarLander-1-ppo", filename="{MODEL FILENAME}.zip", )