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--- |
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library_name: sample-factory |
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tags: |
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- deep-reinforcement-learning |
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- reinforcement-learning |
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- sample-factory |
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model-index: |
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- name: APPO |
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results: |
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- task: |
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type: reinforcement-learning |
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name: reinforcement-learning |
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dataset: |
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name: ant |
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type: ant |
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metrics: |
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- type: mean_reward |
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value: 12565.17 +/- 3350.51 |
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name: mean_reward |
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verified: false |
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--- |
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A(n) **APPO** model trained on the **ant** 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 apetrenko/sample_factory_brax_ant |
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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 sf_examples.brax.enjoy_brax --algo=APPO --env=ant --train_dir=./train_dir --experiment=sample_factory_brax_ant |
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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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## 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 sf_examples.brax.train_brax --algo=APPO --env=ant --train_dir=./train_dir --experiment=sample_factory_brax_ant --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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