Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Axwl/LunarLanderShip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Axwl/LunarLanderShip with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Axwl/LunarLanderShip", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download first_try_lander.zip from Axwl/LunarLanderShip: direct link, hf CLI and curl.
- Browser
- Download file 143 kB
-
https://huggingface.co/Axwl/LunarLanderShip/resolve/main/first_try_lander.zip
- Command line
-
hf download hf://Axwl/LunarLanderShip/first_try_lander.zip
-
curl -L -o first_try_lander.zip https://huggingface.co/Axwl/LunarLanderShip/resolve/main/first_try_lander.zip
143 kB
- Xet hash:
- a680cfdd5195a4f448b7de8cb8463de939f4b84000570a5a53c0a01c42350667
- Size of remote file:
- 143 kB
- SHA256:
- c3f69ab75ca2ec5aa4a5f31c93b52370e905815f9773f8eb3b5816095b40bdaa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.