Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
RiverraidNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_riverraid_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_riverraid_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_riverraid_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
Download checkpoint_p0/milestones/checkpoint_000239472_490438656.pth from edbeeching/atari_2B_atari_riverraid_1111: direct link, hf CLI and curl.
- Browser
- Download file 20.8 MB
-
https://huggingface.co/edbeeching/atari_2B_atari_riverraid_1111/resolve/main/checkpoint_p0/milestones/checkpoint_000239472_490438656.pth
- Command line
-
hf download hf://edbeeching/atari_2B_atari_riverraid_1111/checkpoint_p0/milestones/checkpoint_000239472_490438656.pth
-
curl -L -o checkpoint_000239472_490438656.pth https://huggingface.co/edbeeching/atari_2B_atari_riverraid_1111/resolve/main/checkpoint_p0/milestones/checkpoint_000239472_490438656.pth
20.8 MB
- Xet hash:
- 63645f703f38f452c07b5b5df8f54d496f5f87c894226aec7759bb4e500d9453
- Size of remote file:
- 20.8 MB
- SHA256:
- a95cfb77c54068ff2516fc835ee3bb1eba49338c168fdf5b2103f1a9ab7e3dce
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