Summary of Exploiting Adjacent Similarity in Multi-armed Bandit Tasks Via Transfer Of Reward Samples, by Nr Rahul et al.
Exploiting Adjacent Similarity in Multi-Armed Bandit Tasks via Transfer of Reward Samples
by NR Rahul, Vaibhav Katewa
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 two algorithms based on Upper Confidence Bound (UCB) to solve sequential multi-task problems, where each task is modeled as a stochastic multi-armed bandit with K arms. The algorithms transfer reward samples from preceding tasks to improve the overall regret across all tasks. The analysis shows that transferring samples reduces the regret compared to not transferring at all or using a naive approach. Empirical results demonstrate performance improvements over standard UCB and naive transfer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to make better decisions when you have many similar problems to solve. Imagine you’re playing a game where you need to choose between different options, and each option has a reward or penalty associated with it. The goal is to find the best option most of the time. The authors propose new ways to transfer knowledge from previous games to future ones, which helps make better decisions overall. |
Keywords
» Artificial intelligence » Multi task
