Summary of Decision Mamba: Reinforcement Learning Via Sequence Modeling with Selective State Spaces, by Toshihiro Ota
Decision Mamba: Reinforcement Learning via Sequence Modeling with Selective State Spaces
by Toshihiro Ota
First submitted to arxiv on: 29 Mar 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 Decision Transformer, a promising approach that applies Transformer architectures to reinforcement learning, relies on causal self-attention to model sequences of states, actions, and rewards. By integrating the Mamba framework, known for its advanced capabilities in efficient and effective sequence modeling, into the Decision Transformer architecture, this study aims to enhance performance in sequential decision-making tasks. The integration is evaluated through a series of experiments across various environments, comparing the modified Decision Transformer, Decision Mamba, with its traditional counterpart. This work contributes to advancing sequential decision-making models, highlighting the potential impact of neural network architecture and training methodology on performance in complex tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how combining two AI techniques can improve decision-making. It’s like a team effort! They take one approach that does well with sequences (Mamba) and another that helps make good choices (Decision Transformer). By mixing them, they want to see if it makes things better. They tested this mix in different situations and compared the results to just using one or the other. What they found is important for making AI decisions, especially when there are lots of steps involved. |
Keywords
* Artificial intelligence * Neural network * Reinforcement learning * Self attention * Transformer
