Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
EnduroNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_enduro_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_enduro_3333 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_enduro_3333 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
Download replay.mp4 from edbeeching/atari_2B_atari_enduro_3333: direct link, hf CLI and curl.
- Browser
- Download file 9.33 MB
-
https://huggingface.co/edbeeching/atari_2B_atari_enduro_3333/resolve/main/replay.mp4
- Command line
-
hf download hf://edbeeching/atari_2B_atari_enduro_3333/replay.mp4
-
curl -L -o replay.mp4 https://huggingface.co/edbeeching/atari_2B_atari_enduro_3333/resolve/main/replay.mp4
9.33 MB
- Xet hash:
- d89d411f4da4d4a8a4da8504cbfbc0b9eef9c4e55e4b9be4216aacc9c58ea793
- Size of remote file:
- 9.33 MB
- SHA256:
- ee46f82809b550a48321b2bae4e4425eeb4b8f45145fd8ae050a11546dd294fc
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