Summary of Learning Memory Mechanisms For Decision Making Through Demonstrations, by William Yue et al.
Learning Memory Mechanisms for Decision Making through Demonstrations
by William Yue, Bo Liu, Peter Stone
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 introduces a novel approach to decision-making in Partially Observable Markov Decision Processes, leveraging memory dependency pairs to capture an expert’s memory mechanisms. By introducing AttentionTuner, a Transformer-based model that incorporates these pairs, significant improvements are found across various tasks compared to standard Transformers. This is demonstrated through evaluations on the Memory Gym and Long-term Memory Benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us better understand how experts make decisions by remembering important events from their past experiences. It develops a new way of training machines to learn from these memories, which improves their ability to make good decisions in uncertain situations. This is important because it can help machines work more like humans and make better choices. |
Keywords
* Artificial intelligence * Transformer
