readme: ---
  library_name: sample-factory
  tags:
  - deep-reinforcement-learning
  - reinforcement-learning
  - sample-factory
  model-index:
  - name: APPO
    results:
    - task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: ant
        type: ant
      metrics:
      - type: mean_reward
        value: 12565.17 +/- 3350.51
        name: mean_reward
        verified: false
  ---
  
  A(n) **APPO** model trained on the **ant** 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 apetrenko/sample_factory_brax_ant
  ```
  
      
  ## Using the model
  
  To run the model after download, use the `enjoy` script corresponding to this environment:
  ```
  python -m sf_examples.brax.enjoy_brax --algo=APPO --env=ant --train_dir=./train_dir --experiment=sample_factory_brax_ant
  ```
  
  
  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.brax.train_brax --algo=APPO --env=ant --train_dir=./train_dir --experiment=sample_factory_brax_ant --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.
      