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--- |
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pipeline_tag: reinforcement-learning |
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tags: |
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- deep |
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- reinforcement |
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- learning |
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- world |
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- models |
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library_name: pytorch |
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license: gpl-3.0 |
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--- |
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# M<sup>3</sup>: A Modular World Model over Streams of Tokens |
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📄 [Paper](https://arxiv.org/abs/2502.11537) ▪️ 💾 [Code](https://github.com/leor-c/M3) ▪️ 🧠 [Trained Model Weights](https://huggingface.co/leorc/M3) |
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M<sup>3</sup> is a modular world model that extends the token-based world model framework to handle diverse observation and action modalities through independent, modality-specific components. It incorporates improvements from existing literature to enhance agent performance and achieves state-of-the-art sample efficiency for planning-free world models. It is the first method of this kind to reach a human-level median score on Atari 100K, exhibiting superhuman performance on 13 games. The model weights provided here cover Atari 100K, DeepMind Control Suite Proprioceptive 500K, and Craftax (Symbolic) 1M. |
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<div align="center"> |
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<img src="https://github.com/user-attachments/assets/14734453-38dd-4bc0-a2e0-349e4eec37a2" height="220" /> |
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<img src="https://github.com/user-attachments/assets/11beac5f-f8ee-48a7-94ec-2130087ed060" height="220" /> |
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<img src="https://github.com/user-attachments/assets/a7e89c77-754f-43e3-982c-423c1257846c" height="220" /> |
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<img src="https://github.com/user-attachments/assets/2ae2791a-9aec-4649-88ad-56e9840cd6b1" height="220" /> |
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<img src="https://github.com/user-attachments/assets/2d5f9c98-2468-4206-95ac-e4d370798d7e" height="220" /> |
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</div> |
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<div align="center"> |
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<img src="https://github.com/user-attachments/assets/32f547d1-198c-4cf4-8f4e-9f683250399a" height="350" /> |
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</div> |
