We study the convergence properties of decentralized fictitious play (DFP) for the class of near-potential games where the incentives of agents are nearly aligned with a potential function. In DFP, agents share information only with their current neighbors in a sequence of time-varying networks, keep estimates of other agents' empirical frequencies, and take actions to maximize their expected utility functions computed with respect to the estimated empirical frequencies. We show that empirical frequencies of actions converge to a set of strategies with potential function values that are larger than the potential function values obtained by approximate Nash equilibria of the closest potential game. This result establishes that DFP has identical convergence guarantees in near-potential games as the standard fictitious play in which agents observe the past actions of all the other agents.

翻译：我们研究的是将虚拟游戏(DFP)分散到接近潜在游戏类中的近潜在游戏(DFP)的趋同性(DFP),其中代理商的激励几乎与潜在功能相匹配。在DFP中,代理商只与当前邻居在一系列时间变化网络中共享信息,对其它代理商的经验频率进行估计,并采取行动尽量扩大根据估计经验频率计算的预期效用功能。我们显示,实证行动频率与一系列潜在功能值大于最接近潜在游戏的Nash均衡获得的潜在功能值的战略相趋一致。这一结果表明,DFPP在近潜在游戏中具有相同的趋同性保证,作为代理商观察所有其他代理商过去行动的标准假游戏。