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Upload README.md with huggingface_hub

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+ ---
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+ library_name: sample-factory
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+ tags:
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - sample-factory
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+ model-index:
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+ - name: APPO
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+ results:
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+ - task:
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+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: ShadowHand
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+ type: ShadowHand
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+ metrics:
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+ - type: mean_reward
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+ value: 8096.60 +/- 1646.35
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+ name: mean_reward
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+ verified: false
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+ ---
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+
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+ A(n) **APPO** model trained on the **ShadowHand** environment.
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+
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+ This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
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+ Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
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+
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+
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+ ## Downloading the model
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+
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+ After installing Sample-Factory, download the model with:
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+ ```
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+ python -m sample_factory.huggingface.load_from_hub -r edbeeching/ShadowHand_1111
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+ ```
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+
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+
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+ ## Using the model
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+
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+ To run the model after download, use the `enjoy` script corresponding to this environment:
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+ ```
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+ python -m sf_examples.isaacgym_examples.enjoy_isaacgym --algo=APPO --env=ShadowHand --train_dir=./train_dir --experiment=ShadowHand_1111
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+ ```
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+
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+
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+ You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
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+ See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
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+
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+ ## Training with this model
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+
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+ To continue training with this model, use the `train` script corresponding to this environment:
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+ ```
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+ python -m sf_examples.isaacgym_examples.train_isaacgym --algo=APPO --env=ShadowHand --train_dir=./train_dir --experiment=ShadowHand_1111 --restart_behavior=resume --train_for_env_steps=10000000000
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+ ```
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+
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+ Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
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+