Summary of No-regret Learning Of Nash Equilibrium For Black-box Games Via Gaussian Processes, by Minbiao Han et al.
No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processes
by Minbiao Han, Fengxue Zhang, Yuxin Chen
First submitted to arxiv on: 14 May 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 This paper explores the challenge of finding Nash equilibria in “black-box” games, where agents don’t have direct access to each other’s payoff functions. Despite extensive research on computing Nash equilibria with complete information about the game, there is a shortage of studies on this topic for black-box games. To address this gap, the authors propose a no-regret learning algorithm that leverages Gaussian processes to identify the equilibrium in such games. The proposed approach not only provides theoretical guarantees but also demonstrates effectiveness across various collections of games through experimental validation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a good strategy for playing games when we don’t know how other players are scoring points. It’s like trying to figure out what someone else wants without them telling us, just by observing their choices. The researchers looked at this problem and came up with a new way to solve it using special computer models called Gaussian processes. They tested this approach on many different games and found that it works well. |
