Instructions to use MattStammers/appo-atari_freeway-approaching_sota with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use MattStammers/appo-atari_freeway-approaching_sota with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r MattStammers/appo-atari_freeway-approaching_sota -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 24aea20
Update README.md
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README.md
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@@ -60,4 +60,4 @@ This model is nearing SOTA performance for the Freeway environment: https://www.
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The composite score at 10 million timesteps is ~32 which is only two points off SOTA of 34. It appears that with PPO even after 2BN timesteps performance can only reach 33.6 - https://huggingface.co/edbeeching/atari_2B_atari_freeway_3333
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I suspect that as with QR-DQN the SAC and TQC models can reach 34 - they just need more training to do so.
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The composite score at 10 million timesteps is ~32 which is only two points off SOTA of 34. It appears that with PPO even after 2BN timesteps performance can only reach 33.6 - https://huggingface.co/edbeeching/atari_2B_atari_freeway_3333
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I suspect that as with QR-DQN the SAC and TQC models can reach 34 - they just need more training to do so.
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