Summary of Horizon-free Regret For Linear Markov Decision Processes, by Zihan Zhang et al.
Horizon-Free Regret for Linear Markov Decision Processes
by Zihan Zhang, Jason D. Lee, Yuxin Chen, Simon S. Du
First submitted to arxiv on: 15 Mar 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 paper presents a breakthrough in reinforcement learning, achieving regret bounds that are nearly independent of planning horizon. Building on previous work, it provides a horizon-free bound for linear Markov Decision Processes (MDPs) with exponentially large or uncountable transition models. Unlike prior approaches, which estimate the transition model and compute value functions at different time steps, this method directly estimates the value functions and confidence sets using weighted least square estimators. The key innovation lies in a structural lemma that bounds the total variation of inhomogeneous value functions by a polynomial factor of the feature dimension. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make better decisions when we don’t know what might happen next. It’s about finding the best way to learn from experience, even if things get really complicated. The researchers developed a new method that can handle huge amounts of information and still make good choices. Their approach is different from others because it doesn’t try to understand everything at once; instead, it focuses on understanding what’s most important. This could lead to breakthroughs in many areas, like healthcare or finance. |
Keywords
* Artificial intelligence * Reinforcement learning
