Summary of Open Problem: Order Optimal Regret Bounds For Kernel-based Reinforcement Learning, by Sattar Vakili
Open Problem: Order Optimal Regret Bounds for Kernel-Based Reinforcement Learning
by Sattar Vakili
First submitted to arxiv on: 21 Jun 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 A reinforcement learning (RL) paper explores the theoretical aspects of non-linear function approximation using kernel-based prediction. Building on previous work in tabular and linear Markov Decision Process structures, this approach naturally extends linear methods and explains neural-network-based models at their infinite width limit. However, performance guarantees for this case remain an open problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning has been successful in many areas. A new method uses kernel-based prediction to help explain how big neural networks work. This is important because we don’t fully understand how these networks make decisions. The research focuses on a problem where we need better guarantees about how well the approach works. |
Keywords
* Artificial intelligence * Neural network * Reinforcement learning
