| --- |
| 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: nethack_challenge |
| type: nethack_challenge |
| metrics: |
| - type: mean_reward |
| value: 3245.47 +/- 2691.37 |
| name: mean_reward |
| verified: false |
| --- |
| |
| A(n) **APPO** model trained on the **nethack_challenge** 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 LLParallax/sample_factory_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=nethack_challenge --train_dir=./train_dir --experiment=sample_factory_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 |
| |
| ## Training with this model |
| |
| To continue training with this model, use the `train` script corresponding to this environment: |
| ``` |
| python -m sf_examples.nethack.train_nethack --env=nethack_challenge --character=mon-hum-neu-mal --num_workers=16 --num_envs_per_worker=32 batch_size=4096 --train_dir=./train_dir --experiment=sample_factory_human_monk --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. |
| |
