Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
AsteroidsNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_asteroid_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_asteroid_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_asteroid_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a641dc06a34b53d65b3ebdaae1e17e3b236f4748e3f235069828ad72970619ee
- Size of remote file:
- 2.22 MB
- SHA256:
- 5eec72d958a291dd99a2baf5bc5e88172ac9dad27a88b5ea1603c5aa52bb91ef
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.