Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use AkkyMa/SpaceInvaders with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use AkkyMa/SpaceInvaders with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="AkkyMa/SpaceInvaders", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download train_eval_metrics.zip from AkkyMa/SpaceInvaders: direct link, hf CLI and curl.
- Browser
- Download file 36.7 kB
-
https://huggingface.co/AkkyMa/SpaceInvaders/resolve/main/train_eval_metrics.zip
- Command line
-
hf download hf://AkkyMa/SpaceInvaders/train_eval_metrics.zip
-
curl -L -o train_eval_metrics.zip https://huggingface.co/AkkyMa/SpaceInvaders/resolve/main/train_eval_metrics.zip
36.7 kB
- Xet hash:
- c829ff6ec6e5859a52a1952a10f80a9303ece7747cb62e169ba65a341af1b557
- Size of remote file:
- 36.7 kB
- SHA256:
- 2c8256651c3b3baeef529146442a5c0310b818c9a69e3d3879b1919710259263
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.