Exponential Lower Bounds for Fictitious Play in Potential Games
Abstract
Fictitious Play (FP) is a simple and natural dynamic for repeated play with many applications in game theory and multi-agent reinforcement learning. It was introduced by Brown and its convergence properties for two-player zero-sum games was established later by Robinson. Potential games [Monderer and Shapley 1996] is another class of games which exhibit the FP property [Monderer and Shapley 1996], i.e., FP dynamics converges to a Nash equilibrium if all agents follows it. Nevertheless, except for two-player zero-sum games and for specific instances of payoff matrices [Abernethy et. al. 2021] or for adversarial tie-breaking rules [Daskalakis and Pan, 2014], the \textit{convergence rate} of FP is unknown. In this work, we focus on the rate of convergence of FP when applied to potential games and more specifically identical payoff games. We prove that FP can take exponential time (in the number of strategies) to reach a Nash equilibrium, even if the game is restricted to \textit{two agents}. To prove this, we recursively construct a two-player coordination game with a unique Nash equilibrium. Moreover, every approximate Nash equilibrium in the constructed game must be close to the pure Nash equilibrium in $\ell_1$-distance.
Cite
Text
Panageas et al. "Exponential Lower Bounds for Fictitious Play in Potential Games." Neural Information Processing Systems, 2023.Markdown
[Panageas et al. "Exponential Lower Bounds for Fictitious Play in Potential Games." Neural Information Processing Systems, 2023.](https://mlanthology.org/neurips/2023/panageas2023neurips-exponential/)BibTeX
@inproceedings{panageas2023neurips-exponential,
title = {{Exponential Lower Bounds for Fictitious Play in Potential Games}},
author = {Panageas, Ioannis and Patris, Nikolas and Skoulakis, Stratis and Cevher, Volkan},
booktitle = {Neural Information Processing Systems},
year = {2023},
url = {https://mlanthology.org/neurips/2023/panageas2023neurips-exponential/}
}