Instructions to use wmFrank/sample-factory-2-atari-pong with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use wmFrank/sample-factory-2-atari-pong with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r wmFrank/sample-factory-2-atari-pong -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
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Download README.md from wmFrank/sample-factory-2-atari-pong: direct link, hf CLI and curl.
- Browser
- Download file 545 Bytes
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https://huggingface.co/wmFrank/sample-factory-2-atari-pong/resolve/main/README.md
- Command line
-
hf download hf://wmFrank/sample-factory-2-atari-pong/README.md
-
curl -L -o README.md https://huggingface.co/wmFrank/sample-factory-2-atari-pong/resolve/main/README.md
545 Bytes
metadata
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- metrics:
- type: mean_reward
value: 13.50 +/- 7.43
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_pong
type: atari_pong
A(n) APPO model trained on the atari_pong environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory