论文标题
关于潜在游戏中纳什均衡寻求算法的收敛速率，信息延迟
On the Convergence Rates of A Nash Equilibrium Seeking Algorithm in Potential Games with Information Delays
论文作者
论文摘要
本文研究了在反馈延迟存在下具有连续策略空间的潜在游戏算法的均衡收敛性能，这是损害各种优化方案的性能的多机构系统中的主要挑战。所提出的算法建立在加速梯度下降方法的改进版本上。我们将其扩展到分散的多代理方案，并为其配备延迟的反馈利用方案。通过适当调整步骤大小并研究延迟函数和步进尺寸之间的相互作用，我们将所提出算法的收敛速率得出到了电位延迟的增长时，及时延迟的趋势是均方根，线性和超线性上限。最后，进行路由游戏的模拟是为了验证我们的发现。
This paper investigates the equilibrium convergence properties of a proposed algorithm for potential games with continuous strategy spaces in the presence of feedback delays, a main challenge in multi-agent systems that compromises the performance of various optimization schemes. The proposed algorithm is built upon an improved version of the accelerated gradient descent method. We extend it to a decentralized multi-agent scenario and equip it with a delayed feedback utilization scheme. By appropriately tuning the step sizes and studying the interplay between delay functions and step sizes, we derive the convergence rates of the proposed algorithm to the optimal value of the potential function when the growth of the feedback delays in time is subject to sublinear, linear, and superlinear upper bounds. Finally, simulations of a routing game are performed to empirically verify our findings.
