Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
BeamRiderNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_beamrider_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_beamrider_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_beamrider_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0caa6925c6dae45275c64dc393800cc5bb48d7bb89e53cf2cd3ade42c6e1392c
- Size of remote file:
- 6.99 MB
- SHA256:
- 2b520e9371e2316ba1b30be3956f99b7009a8ed8c8c96699edf0cc45cf89556c
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