Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use lobonexequiel/reinforcement-learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use lobonexequiel/reinforcement-learning with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="lobonexequiel/reinforcement-learning", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download model1.zip from lobonexequiel/reinforcement-learning: direct link, hf CLI and curl.
- Browser
- Download file 147 kB
-
https://huggingface.co/lobonexequiel/reinforcement-learning/resolve/main/model1.zip
- Command line
-
hf download hf://lobonexequiel/reinforcement-learning/model1.zip
-
curl -L -o model1.zip https://huggingface.co/lobonexequiel/reinforcement-learning/resolve/main/model1.zip
147 kB
- Xet hash:
- 391c3396aab6ba1b3eabc836c2b768c80a5f4ceed8a3a5b8371d96038f2d6aba
- Size of remote file:
- 147 kB
- SHA256:
- 530f8d12143a65e2adf39a13a79c0cb5b867d45f42fbbf1b658182ac80fc8691
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.