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arXiv 2201.11324cs.GT
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无信息交换的随机非合作博弈高效分布式学习

Efficient Distributed Learning in Stochastic Non-cooperative Games without Information Exchange

Haidong Li, Anzhi Sheng, Yijie Peng, Long Wang

AI总结:

针对需信息交换的现有随机非合作博弈纳什均衡求解方法的局限,提出无需参与者通信的随机分布式学习算法,结合同时扰动梯度估计与镜像下降,证明其在严格单调博弈中收敛速率更优,经数值实验验证有效。

摘要:

本研究聚焦随机非合作博弈场景,该场景下每个参与者仅能通过带噪黑盒函数评估来估计自身成本函数。由于每个参与者的成本函数同时取决于自身决策变量与对手的决策变量,现有求解纳什均衡的工作大多需要通过中心节点或网络交换局部信息。我们提出一种全新的随机分布式学习算法,无需参与者之间进行通信。该算法采用同时扰动方法估计各成本函数的梯度,并使用镜像下降方法搜索纳什均衡。我们对梯度估计的偏差和方差进行了渐近分析,证明在严格单调博弈类别中,所提算法能以均方意义收敛到纳什均衡,且收敛速率快于现有算法。数值实验结果验证了所提方法的有效性。

英文摘要:

In this work, we study stochastic non-cooperative games, where only noisy black-box function evaluations are available to estimate the cost function for each player. Since each player's cost function depends on both its own decision variables and its rivals' decision variables, local information needs to be exchanged through a center/network in most existing work for seeking the Nash equilibrium. We propose a new stochastic distributed learning algorithm that does not require communications among players. The proposed algorithm uses simultaneous perturbation method to estimate the gradient of each cost function, and uses mirror descent method to search for the Nash equilibrium. We provide asymptotic analysis for the bias and variance of gradient estimates, and show the proposed algorithm converges to the Nash equilibrium in mean square for the class of strictly monotone games at a rate faster than the existing algorithms. The effectiveness of the proposed method is buttressed in a numerical experiment.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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