Summary of Integrating Multi-modal Input Token Mixer Into Mamba-based Decision Models: Decision Metamamba, by Wall Kim
Integrating Multi-Modal Input Token Mixer Into Mamba-Based Decision Models: Decision MetaMamba
by Wall Kim
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the potential of State Space models (SSMs) to surpass Decision Transformer variants in offline reinforcement learning (RL). While initial experiments showed promise with SSMs, further analysis revealed that decision models based on Mamba, a state-of-the-art SSM, failed to outperform enhanced Decision Transformers. To address this limitation, the authors propose Decision MetaMamba (DMM), which incorporates a token mixer in its input layer to account for multimodal offline RL inputs. DMM demonstrates improved performance while reducing parameter count, highlighting the importance of preserving information from proximate time steps rather than specific design. This work enhances Mamba’s performance in offline RL, characterized by memory efficiency and fast inference, opening avenues for broader application. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers can make good decisions without actually doing things. They compared two ways of making these decisions: using State Space models or Decision Transformers. At first, the State Space models seemed better, but then they realized that’s not entirely true. To fix this problem, they created a new way to use the State Space model, called Decision MetaMamba. This new approach worked better and used less memory than before. It’s like having a superpower for computers to make smart decisions. |
Keywords
» Artificial intelligence » Inference » Reinforcement learning » Token » Transformer
