Summary of Why Long Model-based Rollouts Are No Reason For Bad Q-value Estimates, by Philipp Wissmann et al.
Why long model-based rollouts are no reason for bad Q-value estimates
by Philipp Wissmann, Daniel Hein, Steffen Udluft, Volker Tresp
First submitted to arxiv on: 16 Jul 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 This abstract discusses the effectiveness of using model-based offline reinforcement learning with long model rollouts, despite some criticism suggesting this approach leads to compounding errors. The paper aims to demonstrate that these long rollouts do not necessarily result in exponentially growing errors and can even produce better Q-value estimates than model-free methods. This work has potential implications for enhancing reinforcement learning techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at using a special kind of computer learning called model-based offline reinforcement learning. Some people think this way is bad because it gets worse over time, but others have had success with it in real-life situations. The goal is to show that even when we use the long versions of these models, they still work well and might even be better than other types of learning. This could help make reinforcement learning more powerful. |
Keywords
* Artificial intelligence * Reinforcement learning
