Distributed Algorithms for Searching Generalized Nash Equilibrium of Noncooperative Games
Distributed Algorithms for Searching Generalized Nash Equilibrium of Noncooperative Games
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DOI：
10.1109/tcyb.2018.2828118
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发表时间：
2019-06
影响因子：
11.8
通讯作者：
Kaihong Lu;Gangshan Jing;Long Wang
中科院分区：
文献类型：
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作者：
Kaihong Lu;Gangshan Jing;Long Wang
本文研究了分布式纳什均衡(NE)搜索问题，其中可行行动集由非线性不等式和线性方程组约束。与已有的大多数分布式网元搜索问题的研究不同，我们考虑了代价函数和可行动作集都依赖于所有参与者的行为，并且每个参与者只能访问其邻居的信息的情况。为了解决这一问题，提出了一种基于连续时间分布式梯度的投影算法，其中每个参与者使用跟随领导者的共识算法来估计其他参与者的动作。在对代价函数和图作适当的假设下，证明了博弈者的行为渐近收敛于一个广义NE。给出了仿真算例，验证了理论结果的有效性。
In this paper, the distributed Nash equilibrium (NE) searching problem is investigated, where the feasible action sets are constrained by nonlinear inequalities and linear equations. Different from most of the existing investigations on distributed NE searching problems, we consider the case where both cost functions and feasible action sets depend on actions of all players, and each player can only have access to the information of its neighbors. To address this problem, a continuous-time distributed gradient-based projected algorithm is proposed, where a leader-following consensus algorithm is employed for each player to estimate actions of others. Under mild assumptions on cost functions and graphs, it is shown that players’ actions asymptotically converge to a generalized NE. Simulation examples are presented to demonstrate the effectiveness of the theoretical results.