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/policy.optimizer.pth from Axwl/LunarLanderShip: direct link, hf CLI and curl.
- Browser
- Download file 84.9 kB
-
https://huggingface.co/Axwl/LunarLanderShip/resolve/main/first_try_lander/policy.optimizer.pth
- Command line
-
hf download hf://Axwl/LunarLanderShip/first_try_lander/policy.optimizer.pth
-
curl -L -o policy.optimizer.pth https://huggingface.co/Axwl/LunarLanderShip/resolve/main/first_try_lander/policy.optimizer.pth
84.9 kB
- Xet hash:
- aa147b483109b1b489e41bbcebe6093062e321640c8d99e9fe50821b6478b1c6
- Size of remote file:
- 84.9 kB
- SHA256:
- 142e9f7ba359ea79772d3005717a948681d0ec7d9c3f6dd14d42d4ad2540fd6c
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