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edbeeching/AnymalTerrain_1111
AnymalTerrain_1111 is a reinforcement learning model from edbeeching. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for sample-factory.
A(n) APPO model trained on the AnymalTerrain environment.
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From the Hugging Face model README
A(n) APPO model trained on the AnymalTerrain 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/
After installing Sample-Factory, download the model with:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/AnymalTerrain_1111
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=AnymalTerrain --train_dir=./train_dir --experiment=AnymalTerrain_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
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=AnymalTerrain --train_dir=./train_dir --experiment=AnymalTerrain_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.