Summary of Optimistic Regret Bounds For Online Learning in Adversarial Markov Decision Processes, by Sang Bin Moon et al.
Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes
by Sang Bin Moon, Abolfazl Hashemi
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 In this research paper, the authors introduce a new framework called Adversarial Markov Decision Process (AMDP) to deal with unknown and varying tasks in decision-making applications like robotics and recommendation systems. The existing AMDP formalism has limitations, particularly when analyzing regret, which can be pessimistic. To address this, the authors propose a variant of AMDP that minimizes regret using cost predictors. They develop a policy search method that achieves sublinear optimistic regret with high probability, which is essential for real-world applications. This framework leverages cost predictors and enables high-probability regret analysis without imposing restrictive assumptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new learning framework called Adversarial Markov Decision Process (AMDP) to handle unknown and changing tasks in decision-making applications like robotics and recommendation systems. The existing AMDP formalism has limitations when analyzing regret, which can be pessimistic. To address this, the authors propose a variant of AMDP that uses cost predictors to minimize regret. They develop a policy search method that achieves sublinear optimistic regret with high probability. |
Keywords
» Artificial intelligence » Probability
