%0 Journal Article
%A FAN Wenxin
%A HAN Mengyun
%A TAO Xueli
%A ZHENG Yanbin
%T Multi-agent collaborative pursuit algorithm based on game theory and Q-learning
%D 2020
%R 10.11772/j.issn.1001-9081.2019101783
%J Journal of Computer Applications
%P 1613-1620
%V 40
%N 6
%X The multi-agent collaborative pursuit problem is a typical problem in the multi-agent coordination and collaboration research. Aiming at the pursuit problem of single escaper with learning ability, a multi-agent collaborative pursuit algorithm based on game theory and Q-learning was proposed. Firstly, a cooperative pursuit team was established and a game model of cooperative pursuit was built. Secondly, through the learning of the escaper’s strategy choices, the trajectory of the escaper’s limited Step-T cumulative reward was established, and the trajectory was adjusted to the pursuer’s strategy set. Finally, the Nash equilibrium solution was obtained by solving the cooperative pursuit game, and the equilibrium strategy was executed by each agent to complete the pursuit task. At the same time, in order to solve the problem that there may be multiple equilibrium solutions, the virtual action behavior selection algorithm was added to select the optimal equilibrium strategy. C# simulation experiments show that, the proposed algorithm can effectively solve the pursuit problem of single escaper with learning ability in the obstacle environment, and the comparative analysis of experimental data shows that the pursuit efficiency of the algorithm under the same conditions is better than that of pure game or pure learning.
%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2019101783