Online Adaptive Dynamic Programming-Based Solution of Networked Multiple-Pursuer and Single-Evader Game

被引：4

作者：

Gong, Zifeng ^{[1
]}

He, Bing ^{[1
]}

Hu, Chen ^{[1
]}

Zhang, Xiaobo ^{[1
]}

Kang, Weijie ^{[1
]}

机构：

[1] PLA Rocket Force Univ Engn, Dept Nucl Engn, Xian 710025, Peoples R China

来源：

ELECTRONICS | 2022年 / 11卷 / 21期

关键词：

multi-agent pursuit-evasion game; differential game; adaptive dynamic programming; policy iteration; value function approximation; NONLINEAR-SYSTEMS; STRATEGIES;

D O I：

10.3390/electronics11213583

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a new scheme for the online solution of a networked multi-agent pursuit-evasion game based on an online adaptive dynamic programming method. As a multi-agent in the game can form an Internet of Things (IoT) system, by incorporating the relative distance and the control energy as the performance index, the expression of the policies when the agents reach the Nash equilibrium is obtained and proved by the minmax principle. By constructing a Lyapunov function, the capture conditions of the game are obtained and discussed. In order to enable each agent to obtain the policy for reaching the Nash equilibrium in real time, the online adaptive dynamic programming method is used to solve the game problem. Furthermore, the parameters of the neural network are fitted by value function approximation, which avoids the difficulties of solving the Hamilton-Jacobi-Isaacs equation, and the numerical solution of the Nash equilibrium is obtained. Simulation results depict the feasibility of the proposed method for use on multi-agent pursuit-evasion games.

引用

页数：20

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