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README.md

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