Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use AdonaiHS/atari-game-Space-Invaders with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use AdonaiHS/atari-game-Space-Invaders with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="AdonaiHS/atari-game-Space-Invaders", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download train_eval_metrics.zip from AdonaiHS/atari-game-Space-Invaders: direct link, hf CLI and curl.
- Browser
- Download file 38.7 kB
-
https://huggingface.co/AdonaiHS/atari-game-Space-Invaders/resolve/main/train_eval_metrics.zip
- Command line
-
hf download hf://AdonaiHS/atari-game-Space-Invaders/train_eval_metrics.zip
-
curl -L -o train_eval_metrics.zip https://huggingface.co/AdonaiHS/atari-game-Space-Invaders/resolve/main/train_eval_metrics.zip
38.7 kB
- Xet hash:
- a5d37710eefc748c9eca46d56d8ed06793735ef22d08a63f628a67cc83fec00d
- Size of remote file:
- 38.7 kB
- SHA256:
- b592ae1e5d3f150fbd6683f6c2aac37709c7abf6dbbe36b5049e440d22186a10
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.