Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
BowlingNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_bowling_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_bowling_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_bowling_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dcf060870947c715b0ad06816032357e8857a5463a5b3e678073f2125cfc7db4
- Size of remote file:
- 6.98 MB
- SHA256:
- 21eb80c8f2470ae5eefd5cd95dec7703eb893b40fce2bf311ed0477db68d4fe7
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