Summary of In-context Decision Transformer: Reinforcement Learning Via Hierarchical Chain-of-thought, by Sili Huang et al.
In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought
by Sili Huang, Jifeng Hu, Hechang Chen, Lichao Sun, Bo Yang
First submitted to arxiv on: 31 May 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 This paper proposes a new approach to offline reinforcement learning (RL) called In-context Decision Transformer (IDT). IDT is designed to handle online tasks efficiently by reconstructing sequences of high-level decisions rather than low-level actions. This allows it to avoid excessively long sequences and solve online tasks more quickly. The authors demonstrate the effectiveness of IDT in long-horizon tasks, achieving state-of-the-art results compared to current in-context RL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way for computers to learn by making decisions without needing a lot of computing power. It’s called In-context Decision Transformer (IDT). IDT helps computers make better choices by thinking about what they’re going to do instead of just doing things one step at a time. This makes it faster and more efficient than other ways computers can learn. The authors tested IDT and found that it worked really well on big tasks, making it a promising new approach in the field of artificial intelligence. |
Keywords
» Artificial intelligence » Reinforcement learning » Transformer
