Abstract:

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.

Key words: multi unmanned ground vehicle, system effectiveness evaluation, multi agent deep reinforcement learning (MADRL), exploratory analysis simulation