Summary of Optimistic Thompson Sampling For No-regret Learning in Unknown Games, by Yingru Li et al.
Optimistic Thompson Sampling for No-Regret Learning in Unknown Games
by Yingru Li, Liangqi Liu, Wenqiang Pu, Hao Liang, Zhi-Quan Luo
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); 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 In this research paper, scientists develop novel machine learning algorithms to tackle complex multi-player scenarios where the environment is unknown. The algorithms are based on Thompson Sampling (TS) and exploit information about opponents’ actions and reward structures. This approach leads to significant reductions in experimental budgets, achieving over tenfold improvements compared to conventional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explains how it’s possible to learn from games without knowing the rules. It uses special computer algorithms that get better with practice. The authors show that their method can help solve problems like finding the best route for traffic or detecting objects in radar signals. |
Keywords
* Artificial intelligence * Machine learning
