Summary of Multi-player Approaches For Dueling Bandits, by or Raveh et al.
Multi-Player Approaches for Dueling Bandits
by Or Raveh, Junya Honda, Masashi Sugiyama
First submitted to arxiv on: 25 May 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 This paper addresses a long-standing issue in distributed systems: multi-armed bandits with preference-based information like human feedback. The multiplayer dueling bandit problem is crucial for scenarios where exploration and exploitation are intertwined, but it has received limited attention. To fill this gap, the authors propose two novel approaches: (1) a Follow Your Leader black-box method that matches the lower bound when utilizing known dueling bandit algorithms as a foundation; and (2) a message-passing fully distributed approach with a Condorcet-winner recommendation protocol, which accelerates exploration in many cases. Experimental comparisons reveal that the multiplayer algorithms outperform single-player benchmarks, demonstrating their efficacy in addressing the nuanced challenges of this setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to make good decisions when we don’t have all the information. Imagine you’re trying to find the best movie or restaurant with friends, and you only get feedback from each other. This is called a “multi-armed bandit” problem because you need to balance exploring new options and sticking with what you know. The authors came up with two new ways to solve this problem: one that uses existing algorithms and another that involves sharing information between people. They tested these methods and found that they work better than previous approaches, which is important for making good decisions in situations where we don’t have all the facts. |
Keywords
» Artificial intelligence » Attention
