Instructions to use wmFrank/sample-factory-2-atari-pong with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use wmFrank/sample-factory-2-atari-pong with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r wmFrank/sample-factory-2-atari-pong -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
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Download README.md from wmFrank/sample-factory-2-atari-pong: direct link, hf CLI and curl.
- Browser
- Download file 545 Bytes
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https://huggingface.co/wmFrank/sample-factory-2-atari-pong/resolve/main/README.md
- Command line
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hf download hf://wmFrank/sample-factory-2-atari-pong/README.md
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curl -L -o README.md https://huggingface.co/wmFrank/sample-factory-2-atari-pong/resolve/main/README.md
545 Bytes
| library_name: sample-factory | |
| tags: | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| - sample-factory | |
| model-index: | |
| - name: APPO | |
| results: | |
| - metrics: | |
| - type: mean_reward | |
| value: 13.50 +/- 7.43 | |
| name: mean_reward | |
| task: | |
| type: reinforcement-learning | |
| name: reinforcement-learning | |
| dataset: | |
| name: atari_pong | |
| type: atari_pong | |
| A(n) **APPO** model trained on the **atari_pong** environment. | |
| This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory | |