Summary of Multi-agent Best Arm Identification in Stochastic Linear Bandits, by Sanjana Agrawal et al.
Multi-Agent Best Arm Identification in Stochastic Linear Bandits
by Sanjana Agrawal, Saúl A. Blanco
First submitted to arxiv on: 20 Nov 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 paper proposes novel algorithms for collaborative best-arm identification in stochastic linear bandits under a fixed-budget scenario. In this setting, multiple agents interact with a central server to learn the best arm of a linear bandit instance while minimizing error probability. The authors introduce MaLinBAI-Star and MaLinBAI-Gen algorithms for star networks and generic networks respectively, which utilize an Upper-Confidence-Bound approach. These algorithms demonstrate exponentially decaying error probabilities in allocated time budgets, outperforming existing multi-agent algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem where many agents work together to find the best option from a set of options (arms) in a situation that’s like a game. The agents share information with each other through a central server and use an algorithm to make decisions about which arm to choose. The goal is to find the best arm while making sure not too many mistakes are made. The authors propose two new algorithms for this problem, which work better than existing solutions. |
Keywords
* Artificial intelligence * Probability
