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@@ -24,33 +24,4 @@ A(n) **APPO** model trained on the **atari_journeyescape** environment.
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  This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
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  Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
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- ## Downloading the model
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- After installing Sample-Factory, download the model with:
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- ```
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- python -m sample_factory.huggingface.load_from_hub -r ksridhar/atari_2B_atari_journeyescape_1111
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- ```
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- ## Using the model
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- To run the model after download, use the `enjoy` script corresponding to this environment:
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- ```
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- python -m <path.to.enjoy.module> --algo=APPO --env=atari_journeyescape --train_dir=./train_dir --experiment=atari_2B_atari_journeyescape_1111
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- ```
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- You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
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- See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
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-
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- ## Training with this model
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- To continue training with this model, use the `train` script corresponding to this environment:
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- ```
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- python -m <path.to.train.module> --algo=APPO --env=atari_journeyescape --train_dir=./train_dir --experiment=atari_2B_atari_journeyescape_1111 --restart_behavior=resume --train_for_env_steps=10000000000
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- ```
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- 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.
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  This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
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  Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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