Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use baronase/unit3-atari-dqn-1m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use baronase/unit3-atari-dqn-1m with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="baronase/unit3-atari-dqn-1m", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download dqn-SpaceInvadersNoFrameskip-v4/policy.optimizer.pth from baronase/unit3-atari-dqn-1m: direct link, hf CLI and curl.
- Browser
- Download file 13.5 MB
-
https://huggingface.co/baronase/unit3-atari-dqn-1m/resolve/main/dqn-SpaceInvadersNoFrameskip-v4/policy.optimizer.pth
- Command line
-
hf download hf://baronase/unit3-atari-dqn-1m/dqn-SpaceInvadersNoFrameskip-v4/policy.optimizer.pth
-
curl -L -o policy.optimizer.pth https://huggingface.co/baronase/unit3-atari-dqn-1m/resolve/main/dqn-SpaceInvadersNoFrameskip-v4/policy.optimizer.pth
13.5 MB
- Xet hash:
- f017ecf5de16283211336da36026e8605a03b1827f8920bf767e220438342e54
- Size of remote file:
- 13.5 MB
- SHA256:
- 06231f3be2be62f05501a5f838ab145c63c0f2c9eedb2b47df8e36f2e6215500
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