Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
ChopperCommandNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_choppercommand_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_choppercommand_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_choppercommand_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f1920b9459d886575df6d10def27a4ceab87a780df65a268ed477833b3d7b39c
- Size of remote file:
- 20.8 MB
- SHA256:
- 3c7db75edb5c44c5311ee013f0bd97106f2a221ca3f8688e5c4bea036f310a3b
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