Summary of The Fallacy Of Minimizing Cumulative Regret in the Sequential Task Setting, by Ziping Xu et al.
The Fallacy of Minimizing Cumulative Regret in the Sequential Task Setting
by Ziping Xu, Kelly W. Zhang, Susan A. Murphy
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: 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 The paper proposes a novel approach to online reinforcement learning (RL) by optimizing two conflicting objectives: cumulative regret (CR) and simple regret (SR). In traditional RL, CR is minimized through interactions with an unknown environment. However, real-world applications involve multiple tasks, where data collected from the first task is used to warm-start subsequent tasks. The performance of these warm-start policies is measured by SR. The authors show that in stationary environments, both CR and SR can be optimized for a specific duration of the task (T). This research has implications for the design of more effective RL algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how we learn new things online without really knowing what will happen next. Right now, we try to make good choices so we don’t lose too much over time. But in real life, we often do things in sequence – like learning one thing and then using that knowledge to learn something else. This is called “warm-starting.” The goal is to make smart decisions so we don’t regret what we did later on. Surprisingly, some experts have found a way to balance these two goals when the situation stays the same. |
Keywords
* Artificial intelligence * Reinforcement learning
