Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
DoubleDunkNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_doubledunk_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_doubledunk_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_doubledunk_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a272326c72dc71029c3f599743f30bbf5ab7e3aefc7a21f6fbb2e524b0e327a
- Size of remote file:
- 7.01 MB
- SHA256:
- 12007d27c110f53e66999cfa2b0bad9713d09edefe7b0a4692ca4d382644969b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.