Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use ravkumar/dqn-SpaceInvadersNoFramSkip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use ravkumar/dqn-SpaceInvadersNoFramSkip with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="ravkumar/dqn-SpaceInvadersNoFramSkip", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download train_eval_metrics.zip from ravkumar/dqn-SpaceInvadersNoFramSkip: direct link, hf CLI and curl.
- Browser
- Download file 41.3 kB
-
https://huggingface.co/ravkumar/dqn-SpaceInvadersNoFramSkip/resolve/main/train_eval_metrics.zip
- Command line
-
hf download hf://ravkumar/dqn-SpaceInvadersNoFramSkip/train_eval_metrics.zip
-
curl -L -o train_eval_metrics.zip https://huggingface.co/ravkumar/dqn-SpaceInvadersNoFramSkip/resolve/main/train_eval_metrics.zip
41.3 kB
- Xet hash:
- 194315377f97e99bf6b2a01f0df739c50d82f3cedb3f20a01f9ccac9bb5fdfda
- Size of remote file:
- 41.3 kB
- SHA256:
- 434e295a584e301fc672edf49dd8e7483ac64832b153860bf7f07660cee2aa4e
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