Instructions to use ksridhar/atari_2B_atari_airraid_1111 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use ksridhar/atari_2B_atari_airraid_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r ksridhar/atari_2B_atari_airraid_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
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: atari_airraid
type: atari_airraid
metrics:
- type: mean_reward
value: 465.00 +/- 182.76
name: mean_reward
verified: false
A(n) APPO model trained on the atari_airraid 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/