Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use nossal/dqn-SpaceInvadersNoFrameskip-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use nossal/dqn-SpaceInvadersNoFrameskip-v4 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="nossal/dqn-SpaceInvadersNoFrameskip-v4", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download train_eval_metrics.zip from nossal/dqn-SpaceInvadersNoFrameskip-v4: direct link, hf CLI and curl.
- Browser
- Download file 17.5 kB
-
https://huggingface.co/nossal/dqn-SpaceInvadersNoFrameskip-v4/resolve/main/train_eval_metrics.zip
- Command line
-
hf download hf://nossal/dqn-SpaceInvadersNoFrameskip-v4/train_eval_metrics.zip
-
curl -L -o train_eval_metrics.zip https://huggingface.co/nossal/dqn-SpaceInvadersNoFrameskip-v4/resolve/main/train_eval_metrics.zip
17.5 kB
- Xet hash:
- 60a0014a7755f30dc9e2525a6f8279f296600052eebf5ae3dfbbb10d1d820982
- Size of remote file:
- 17.5 kB
- SHA256:
- 1a9736bb73f68086bc49d3d93480fc81f4c32386550260af84e9d7a243b450d6
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