Reinforcement Learning
stable-baselines3
English
LunarLander-v2
deep-reinforcement-learning
gymnasium
rl_env
Eval Results (legacy)
Instructions to use prem-ml/LunarLander-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use prem-ml/LunarLander-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="prem-ml/LunarLander-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
PPO Agent playing LunarLander
This is a trained model of a PPO agent playing LunarLander using the stable-baselines3 library.
Results
| Metric | Value |
|---|---|
| Mean reward | 294.11 |
| Std reward | 18.80 |
Usage (with Stable-baselines3)
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
checkpoint = load_from_hub(
repo_id="prem-ml/LunarLander-v3",
filename="ppo-LunarLander-v3.zip",
)
model = PPO.load(checkpoint)
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Evaluation results
- mean_reward on LunarLander-v2self-reported294.11 +/- 18.80