# On the Exponential Rate of Convergence of Fictitious Play in Potential Games

arXiv: https://arxiv.org/abs/1707.08055 · HTML: https://mlsanity.com/paper/1707.08055/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2017.

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

- [Scaling Bayesian Optimization With Game Theory](https://mlsanity.com/paper/2110.03790/) (2021)

## Abstract (arXiv)

The paper studies fictitious play (FP) learning dynamics in continuous time. It is shown that in almost every potential game, and for almost every initial condition, the rate of convergence of FP is exponential. In particular, the paper focuses on studying the behavior of FP in potential games in which all equilibria of the game are regular, as introduced by Harsanyi. Such games are referred to as regular potential games. Recently it has been shown that almost all potential games (in the sense of the Lebesgue measure) are regular. In this paper it is shown that in any regular potential game (and hence, in almost every potential game), FP converges to the set of Nash equilibria at an exponential rate from almost every initial condition.