README.md

---
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: challenge
      type: challenge
    metrics:
    - type: mean_reward
      value: ???
      name: mean_reward
      verified: false
---

A(n) **APPO** model trained on the **challenge** environment.

This model was trained using Sample-Factory 2.0: https://github.com/BartekCupial/sample-factory/tree/nethack.
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 LLParallax/sf_finetuning_forgetting_human_monk
```

    
## Using the model

To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m sf_examples.nethack.enjoy_nethack \
  --env=challenge \
  --character=mon-hum-neu-mal \
  --train_dir=./train_dir \
  --experiment=sf_finetuning_forgetting_human_monk \
  --use_pretrained_checkpoint=False \
  --teacher_path=./train_dir/sf_finetuning_forgetting_human_monk
```

For performance evaluation, use the `eval` script:
```
python -m sf_examples.nethack.eval_nethack \
  --env=challenge \
  --character=mon-hum-neu-mal \
  --sample_env_episodes=128 \
  --num_workers=16 \
  --num_envs_per_worker=32 \
  --worker_num_splits=2 \
  --train_dir=./train_dir \
  --experiment=sf_finetuning_forgetting_human_monk \
  --use_pretrained_checkpoint=False \
  --teacher_path=./train_dir/sf_finetuning_forgetting_human_monk
```

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