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library_name: sample-factory
  - deep-reinforcement-learning
  - reinforcement-learning
  - sample-factory
  - name: APPO
      - task:
          type: reinforcement-learning
          name: reinforcement-learning
          name: doom_health_gathering_supreme
          type: doom_health_gathering_supreme
          - type: mean_reward
            value: 8.70 +/- 3.35
            name: mean_reward
            verified: false

A(n) APPO model trained on the doom_health_gathering_supreme environment.

This model was trained using Sample-Factory 2.0: Documentation for how to use Sample-Factory can be found at

Downloading the model

After installing Sample-Factory, download the model with:

python -m sample_factory.huggingface.load_from_hub -r kitrak-rev/rl_course_vizdoom_health_gathering_supreme

Using the model

To run the model after download, use the enjoy script corresponding to this environment:

python -m <> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme

You can also upload models to the Hugging Face Hub using the same script with the --push_to_hub flag. See for more details

Training with this model

To continue training with this model, use the train script corresponding to this environment:

python -m <> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_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.