| library_name: sample-factory | |
| tags: | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| - sample-factory | |
| A(n) **APPO** model trained on the **GDY-MettaGrid** 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 metta-ai/baseline.v0.2.0 | |
| ``` | |
| ## Using the model | |
| To run the model after download, use the `enjoy` script corresponding to this environment: | |
| ``` | |
| python -m <path.to.enjoy.module> --algo=APPO --env=GDY-MettaGrid --train_dir=./train_dir --experiment=baseline.v0.2.0 | |
| ``` | |
| 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 <path.to.train.module> --algo=APPO --env=GDY-MettaGrid --train_dir=./train_dir --experiment=baseline.v0.2.0 --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. | |