基于EAS+MADRL的多无人车体系效能评估方法研究

无人作战开始步入现代战争舞台, 多无人车(multi unmanned ground vehicle, MUGV) 协同作战将成为未来陆上作战的主要样式。体系效能评估是装备论证和战法研究的核心问题, 针对MUGV体系效能评估问题, 建立了一套以自主学习算法为基础的探索性仿真分析方法。将MUGV对抗过程建模为零和随机博弈(zero sum stochastic game, ZSG)模型, 通过使用多智能体深度强化学习类方法(multi agent deep reinforcement learning, MADRL)探索在不同对方无人车规模条件下, ZSG模型的纳什均衡解, 分析纳什均衡条件下参战双方胜率, 作战时长等约束, 完成MUGV体系作战效能评估, 并在最后给出了MUGV体系效能评估应用示例, 从而建立了更可信、可用的体系效能评估方法。

Research on efficiency evaluation method of multi unmanned ground vehicle system based on EAS+MADRL

Unmanned combat has stepped into the stage of modern war, and multi unmanned ground vehicle (MUGV) cooperative combat will become the main style of land combat in the future. The evaluation of system effectiveness is the core problem of equipment demonstration and combat method research. A set of exploratory simulation analysis method based on multi agent deep reinforcement learning is established to evaluate the combat effectiveness of MUGV system. The MUGV confrontation process is modeled as a zero sum stochastic game (ZSG) model. By using multi-agent deep reinforcement learning MADRL to explore the Nash equilibrium solution of ZSG model under the condition of different scale of enemy unmanned ground vehicle, and analyzing the constraints such as the winning rate and the duration of combat under the condition of Nash equilibrium, the combat effectiveness evaluation of MUGV system is completed. Finally, the application example of operational effectiveness evaluation of MUGV system is given. Thus, a more reliable and available system performance evaluation method is established.