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/pytorch_variables.pth from baronase/unit3-atari-dqn-1m: direct link, hf CLI and curl.
- Browser
- Download file 864 Bytes
-
https://huggingface.co/baronase/unit3-atari-dqn-1m/resolve/main/dqn-SpaceInvadersNoFrameskip-v4/pytorch_variables.pth
- Command line
-
hf download hf://baronase/unit3-atari-dqn-1m/dqn-SpaceInvadersNoFrameskip-v4/pytorch_variables.pth
-
curl -L -o pytorch_variables.pth https://huggingface.co/baronase/unit3-atari-dqn-1m/resolve/main/dqn-SpaceInvadersNoFrameskip-v4/pytorch_variables.pth
864 Bytes
- Xet hash:
- 26b309f4213608810403711cbbac18442fc8fd3702f8ec164c637dd6c4c8278b
- Size of remote file:
- 864 Bytes
- SHA256:
- 0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
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