Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
EnduroNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_enduro_1111 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_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_enduro_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
Download replay.mp4 from edbeeching/atari_2B_atari_enduro_1111: direct link, hf CLI and curl.
- Browser
- Download file 9.26 MB
-
https://huggingface.co/edbeeching/atari_2B_atari_enduro_1111/resolve/main/replay.mp4
- Command line
-
hf download hf://edbeeching/atari_2B_atari_enduro_1111/replay.mp4
-
curl -L -o replay.mp4 https://huggingface.co/edbeeching/atari_2B_atari_enduro_1111/resolve/main/replay.mp4
9.26 MB
- Xet hash:
- a598f6b594eaa277c74051d032ffb64240c425043d772647242693cbf91e3f7b
- Size of remote file:
- 9.26 MB
- SHA256:
- f2724de6652ffccb0ae4271c16ce7592a26fd7771c47952f1c7b6e288825f440
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