Instructions to use Vladprime/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Vladprime/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Vladprime/ppo-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
PPO LunarLander-v2
Trained from a randomly initialized policy for Vlad Kolomiiets (Vladprime), following Unit 1 of the Hugging Face Deep RL Course. No pretrained policy was used.
Training seed: 20261101. Training environment steps: 1,250,000. Algorithm: Stable-Baselines3 PPO on CPU with eight environments.
Final independent evaluation: 200 fixed seeds from 1000000 through 1000199. Mean reward 263.1597, standard deviation 21.4778, mean minus standard deviation 241.6819. Validation used separate seeds 200000โ200039. Full episode returns are saved in results.json.
Reproduce
pip install -r requirements.txt
python train.py
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Evaluation results
- mean_reward on LunarLander-v2self-reported263.1597 +/- 21.4778