A(n) APPO model trained on the atari_bowling 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 MattStammers/adqn-atari_bowling

Using the model

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_bowling --train_dir=./train_dir --experiment=adqn-atari_bowling

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.atari.train_atari --algo=APPO --env=atari_bowling --train_dir=./train_dir --experiment=adqn-atari_bowling --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.