Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
BeamRiderNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_beamrider_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_beamrider_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_beamrider_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0bbf5ff0bbd81cf226154c5155bf4530156dab76a8d226b087b984772a009b5f
- Size of remote file:
- 3.49 MB
- SHA256:
- e3155c95053b68eb58df3fff7eeee216d048c93aae0411e814e851260996fd57
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