Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
EnduroNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_enduro_1111 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use edbeeching/atari_2B_atari_enduro_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_enduro_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
| library_name: sample-factory | |
| tags: | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| - sample-factory | |
| - EnduroNoFrameskip-v4 | |
| model-index: | |
| - name: APPO | |
| results: | |
| - task: | |
| type: reinforcement-learning | |
| name: reinforcement-learning | |
| dataset: | |
| name: atari_enduro | |
| type: atari_enduro | |
| metrics: | |
| - type: mean_reward | |
| value: 2275.55 +/- 200.24 | |
| name: mean_reward | |
| verified: false | |
| A(n) **APPO** model trained on the **atari_enduro** environment. | |
| This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory | |