Summary of Online Mdp with Transition Prototypes: a Robust Adaptive Approach, by Shuo Sun et al.
Online MDP with Transition Prototypes: A Robust Adaptive Approach
by Shuo Sun, Meng Qi, Zuo-Jun Max Shen
First submitted to arxiv on: 18 Dec 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 research proposes a novel approach to robust Markov Decision Processes (MDPs) that leverages prior knowledge of prototypes in the underlying transition kernel. The algorithm efficiently identifies the true kernel while guaranteeing performance of the corresponding robust policy, achieving sublinear regret and providing an early stopping mechanism. Numerical experiments demonstrate improved performance over existing methods, particularly in the early stages with limited data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better decisions when things are uncertain by combining two ideas: Markov Decision Processes (MDPs) and prototypes of what might happen next. We learn more about the situation as we go along, but only use what we know so far to make smart choices. The results show that our method does a great job, especially at first when there’s not much data. |
Keywords
» Artificial intelligence » Early stopping
