Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
AmidarNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_amidar_3333 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use edbeeching/atari_2B_atari_amidar_3333 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_amidar_3333 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c511647eb22c8baac447d5b1df79c5ae84099ff23c828c74a11d50c82071e09
- Size of remote file:
- 6.99 MB
- SHA256:
- 5ee87620469b4e889a95a6dbb4ffab0c8cff45782b0da342afb4c1aa141f70c9
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