Summary of Beating Adversarial Low-rank Mdps with Unknown Transition and Bandit Feedback, by Haolin Liu et al.
Beating Adversarial Low-Rank MDPs with Unknown Transition and Bandit Feedback
by Haolin Liu, Zakaria Mhammedi, Chen-Yu Wei, Julian Zimmert
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates regret minimization in low-rank Markov Decision Processes (MDPs) with fixed transition and adversarial losses. Building upon previous work, the authors improve the regret bound from poly(d, A, H)T^{5/6} to poly(d, A, H)T^{2/3} for the full-information unknown transition setting. Additionally, they propose model-based and model-free algorithms achieving poly(d, A, H)T^{2/3} regret in the bandit loss feedback setting with unknown transitions. The authors also show that a linear structure is necessary for the bandit case without structure on the reward function, resulting in polynomial regret scaling with the number of states. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make better decisions when faced with uncertainty and changing conditions. It’s like trying to find the best path in a maze while someone keeps moving the walls! The authors found ways to improve upon previous methods for making good choices, especially when you don’t know what the outcome will be. They even came up with new approaches that can make better decisions, but they’re not as efficient. The important thing is that this research helps us understand how we can make smarter choices in tricky situations. |