# A Bayesian optimization approach to compute the Nash equilibria of potential games using bandit feedback

arXiv: https://arxiv.org/abs/1811.06503 · HTML: https://mlsanity.com/paper/1811.06503/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processes](https://mlsanity.com/paper/2405.08318/) (2024)

## Abstract (arXiv)

Computing Nash equilibria for strategic multi-agent systems is challenging for expensive black box systems. Motivated by the ubiquity of games involving exploitation of common resources, this paper considers the above problem for potential games. We use the Bayesian optimization framework to obtain novel algorithms to solve finite (discrete action spaces) and infinite (real interval action spaces) potential games, utilizing the structure of potential games. Numerical results illustrate the efficiency of the approach in computing the Nash equilibria of static potential games and linear Nash equilibria of dynamic potential games.