Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
PrivateEyeNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_privateye_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_privateye_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_privateye_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98c3844aca0da11e5a05785f039b5ba3b9ae6eb6830cbc54f9e25c25cb67afa6
- Size of remote file:
- 7.01 MB
- SHA256:
- 8b652d3543b7b06f30722e19c5a6475408c8f42badbfd7f53ea8ab1c47d2f684
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.