Summary of Bounding-box Inference For Error-aware Model-based Reinforcement Learning, by Erin J. Talvitie et al.
Bounding-Box Inference for Error-Aware Model-Based Reinforcement Learning
by Erin J. Talvitie, Zilei Shao, Huiying Li, Jinghan Hu, Jacob Boerma, Rory Zhao, Xintong Wang
First submitted to arxiv on: 23 Jun 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 The paper proposes a novel approach to model-based reinforcement learning, addressing the issue of inaccurate models interfering with policy learning. By incorporating model uncertainty measures, agents can selectively use the learned model only when it provides reliable predictions. The study empirically explores various methods for estimating model uncertainty and shows that bounding-box inference yields the best results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how to make better decisions in complex situations by using a computer program called a “model” to predict what will happen next. Sometimes, this model is not very good, which can cause problems. The researchers looked into ways to figure out when the model is likely to be wrong and then use it only when it’s right. They found that a special method they developed, called “bounding-box inference,” works best for this task. |
Keywords
* Artificial intelligence * Bounding box * Inference * Reinforcement learning
