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
- Xet hash:
- f2abcacd75b583ff70602eb8810ca1529162f9042777269795479dcdc79adf1a
- Size of remote file:
- 20.8 MB
- SHA256:
- cfe8475d12b20b2bc555a58c5f5516ca3871f11dbb7777fe77f7c7150939ec72
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.