Summary of Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?, by Yang Dai et al.
Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?
by Yang Dai, Oubo Ma, Longfei Zhang, Xingxing Liang, Shengchao Hu, Mengzhu Wang, Shouling Ji, Jincai Huang, Li Shen
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel approach to offline Reinforcement Learning (offline RL), dubbed DeMa, is introduced, building upon Mamba, a linear-time sequence model. DeMa leverages Transformer-like architecture, focusing on sequences that diminish approximately exponentially with longer sequences. The hidden attention mechanism is identified as a crucial factor in DeMa’s success, applicable to other residual structures and not requiring position embedding. Comprehensive experiments demonstrate DeMa’s compatibility with trajectory optimization, surpassing previous methods, such as Decision Transformer (DT), in Atari and MuJoCo environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline Reinforcement Learning gets a boost! Researchers developed a new model called DeMa, which is based on Mamba, a fast sequence model. DeMa uses a special kind of architecture that focuses on sequences and makes it work well for robots and drones with limited power. They found that the hidden attention mechanism is key to DeMa’s success and that it can be used in other situations too. The team tested DeMa and showed that it works better than previous models, using fewer parameters! |
Keywords
» Artificial intelligence » Attention » Embedding » Optimization » Reinforcement learning » Sequence model » Transformer
