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Parth673/unit8-doom_health_gathering_supreme
unit8-doom_health_gathering_supreme is a reinforcement learning model from Parth673. 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 doomhealthgatheringsupreme environment.
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Updated Dec 16, 2023
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From the Hugging Face model README
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/
After installing Sample-Factory, download the model with:
python -m sample_factory.huggingface.load_from_hub -r akghxhs55/appo-doom_health_gathering_supreme
To run the model after download, use the enjoy script corresponding to this environment:
python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=appo-doom_health_gathering_supreme
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 .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=appo-doom_health_gathering_supreme --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.