Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
SeaquestNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_seaquest_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_seaquest_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_seaquest_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
A(n) APPO model trained on the atari_seaquest environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
- Downloads last month
- 4
Evaluation results
- mean_reward on atari_seaquestself-reported2834.00 +/- 39.55