Summary of Finding Good Policies in Average-reward Markov Decision Processes Without Prior Knowledge, by Adrienne Tuynman et al.
Finding good policies in average-reward Markov Decision Processes without prior knowledge
by Adrienne Tuynman, Rémy Degenne, Emilie Kaufmann
First submitted to arxiv on: 27 May 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 The abstract discusses the identification of optimal policies in Markov Decision Processes (MDPs) with average rewards. The authors revisit two measures of complexity, diameter and optimal bias span, which are used to study the sample complexity of policy identification. They show that estimating the optimal bias span is not possible without knowing it beforehand, but propose an algorithm for identifying an -optimal policy that does not require prior knowledge. The algorithm’s sample complexity scales with SAD/^2, which is near-optimal. Additionally, the authors provide a lower bound for online policy identification and propose two algorithms with polynomial sample complexities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to find the best policy in a situation where you make decisions based on previous experiences. It looks at two measures that help us understand how complex this problem is. The researchers show that it’s hard to figure out one of these measures without already knowing what it is, but they propose a new way to find the optimal policy that doesn’t require any prior knowledge. This method works well when you want to make good decisions quickly. They also provide a rule for stopping and adjusting based on data. |
