Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
DefenderNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_defender_2222 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use edbeeching/atari_2B_atari_defender_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_defender_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
Download replay.mp4 from edbeeching/atari_2B_atari_defender_2222: direct link, hf CLI and curl.
- Browser
- Download file 5.18 MB
-
https://huggingface.co/edbeeching/atari_2B_atari_defender_2222/resolve/96c8f94cd9507dc5fa96bc9a2d2cbb51acddbebc/replay.mp4
- Command line
-
hf download hf://edbeeching/atari_2B_atari_defender_2222@96c8f94cd9507dc5fa96bc9a2d2cbb51acddbebc/replay.mp4
-
curl -L -o replay.mp4 https://huggingface.co/edbeeching/atari_2B_atari_defender_2222/resolve/96c8f94cd9507dc5fa96bc9a2d2cbb51acddbebc/replay.mp4
5.18 MB
- Xet hash:
- df8936cb704de3262221a676d413133c98b4dfed7e86e9ee84153eadc40136ca
- Size of remote file:
- 5.18 MB
- SHA256:
- ea61f3aa7f87c731e108177fb15432f29db94abe2fb0a302f1140a78d3d2b53e
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