Instructions to use saimani1232/rl_course_vizdoom_health_gathering_supreme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use saimani1232/rl_course_vizdoom_health_gathering_supreme with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r saimani1232/rl_course_vizdoom_health_gathering_supreme -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
A(n) APPO model trained on the doom_health_gathering_supreme 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 saimani1232/rl_course_vizdoom_health_gathering_supreme
Using the model
To run the model after download, use the enjoy script corresponding to this environment:
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
- Downloads last month
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Evaluation results
- mean_reward on doom_health_gathering_supremeself-reported11.97 +/- 4.61