metadata
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