Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
BowlingNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_bowling_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_bowling_3333 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_bowling_3333 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf4eb5677914b7c5daab8804b166520f62302a8099f1d2206128c3235e7d1d2a
- Size of remote file:
- 6.98 MB
- SHA256:
- 349d5dc1024ada58d353b00348f54b7f274177a367191864424068cad8703d9f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.