Instructions to use andrewzhang505/sample-factory-2-mujoco-humanoid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use andrewzhang505/sample-factory-2-mujoco-humanoid with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r andrewzhang505/sample-factory-2-mujoco-humanoid -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
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Download README.md from andrewzhang505/sample-factory-2-mujoco-humanoid: direct link, hf CLI and curl.
- Browser
- Download file 565 Bytes
-
https://huggingface.co/andrewzhang505/sample-factory-2-mujoco-humanoid/resolve/main/README.md
- Command line
-
hf download hf://andrewzhang505/sample-factory-2-mujoco-humanoid/README.md
-
curl -L -o README.md https://huggingface.co/andrewzhang505/sample-factory-2-mujoco-humanoid/resolve/main/README.md
565 Bytes
metadata
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- metrics:
- type: mean_reward
value: 5439.48 +/- 1314.24
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: mujoco_humanoid
type: mujoco_humanoid
A(n) APPO model trained on the mujoco_humanoid environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory