Summary of The Interpretability Of Codebooks in Model-based Reinforcement Learning Is Limited, by Kenneth Eaton et al.
The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited
by Kenneth Eaton, Jonathan Balloch, Julia Kim, Mark Riedl
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 explores the notion that vector quantization methods can provide interpretability in deep reinforcement learning systems. The authors investigate whether this approach yields emergent interpretability and discover that it does not, as the codes generated are inconsistent, non-unique, and have limited impact on concept disentanglement. This challenges the assumption that vector quantization can lead to model-based reinforcement learning interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep reinforcement learning systems could be made more understandable by humans if they were easier to interpret. Some experts think that a way to make this happen is by using special methods called vector quantization, which help us understand how these systems work. But does it really? This research looked into whether vector quantization actually makes things more interpretable and found out it doesn’t because the codes generated aren’t reliable or helpful in understanding how the system works. |
Keywords
» Artificial intelligence » Quantization » Reinforcement learning
