Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
JamesbondNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_jamesbond_3333 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use edbeeching/atari_2B_atari_jamesbond_3333 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_jamesbond_3333 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
Download replay.mp4 from edbeeching/atari_2B_atari_jamesbond_3333: direct link, hf CLI and curl.
- Browser
- Download file 1.06 MB
-
https://huggingface.co/edbeeching/atari_2B_atari_jamesbond_3333/resolve/main/replay.mp4
- Command line
-
hf download hf://edbeeching/atari_2B_atari_jamesbond_3333/replay.mp4
-
curl -L -o replay.mp4 https://huggingface.co/edbeeching/atari_2B_atari_jamesbond_3333/resolve/main/replay.mp4
1.06 MB
- Xet hash:
- 296871792b27b44aefbd80b55de4657e711ae104dd66e3b70d1e3df4933a8279
- Size of remote file:
- 1.06 MB
- SHA256:
- 0d3f0cb1465ab49740e91087f5bd1b51cf917c3ab8b0e8484d0202e272eec886
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