Summary of Return-aligned Decision Transformer, by Tsunehiko Tanaka et al.
Return-Aligned Decision Transformer
by Tsunehiko Tanaka, Kenshi Abe, Kaito Ariu, Tetsuro Morimura, Edgar Simo-Serra
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Return-Aligned Decision Transformer (RADT) addresses a limitation in traditional offline reinforcement learning approaches, where agents are not tailored to meet human requirements. Unlike Decision Transformers (DTs), which optimize policies for cumulative rewards, RADT incorporates features extracted solely from the target return, enabling more consistent action generation aligned with the desired outcome. By leveraging this attention mechanism, RADT reduces discrepancies between actual and target returns in experiments. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary In a breakthrough paper, researchers propose a new AI model that adjusts its behavior to meet human expectations. The Return-Aligned Decision Transformer (RADT) makes decisions based on what we want it to achieve, not just the outcome of those actions. This is important because many AI applications, like video games and educational tools, require agents that can adapt to our needs. |
Keywords
* Artificial intelligence * Attention * Reinforcement learning * Transformer