Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
DoubleDunkNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_doubledunk_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_doubledunk_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_doubledunk_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 271f2fb9e3bc94e0baf5dea0f873b11ab6526f882d0f8f0035f1a195420336ca
- Size of remote file:
- 7.01 MB
- SHA256:
- edfd7acbb0f14302eae7e06b7ca79273266d06ea2496da2591d2db302f285acd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.