Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
CrazyClimberNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_crazyclimber_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_crazyclimber_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_crazyclimber_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a1b1518cc5e2273e1f8c3aeb1fb0c3b837a3ae86a44f16c26b1a3ce751d733e
- Size of remote file:
- 6.99 MB
- SHA256:
- c6206b9c36c494406a8bcc6535219e2d8c95c2287c58ed889de15eeaf20bb6ad
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