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MattStammers/appo-atari-berzerk
appo-atari-berzerk is a reinforcement learning model from MattStammers. 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 atariberzerk environment.
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Updated Sep 24, 2023
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From the Hugging Face model README
A(n) APPO model trained on the atari_berzerk 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 MattStammers/appo-atari-berzerk
To run the model after download, use the enjoy script corresponding to this environment:
python -m sf_examples.atari.enjoy_atari --algo=APPO --env=atari_berzerk --train_dir=./train_dir --experiment=appo-atari-berzerk
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.atari.train_atari --algo=APPO --env=atari_berzerk --train_dir=./train_dir --experiment=appo-atari-berzerk --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.