Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (20): 271-278.

### Application of Deep Reinforcement Learning Algorithm on Intelligent Military Decision System

KUANG Liqun, LI Siyuan, FENG Li, HAN Xie, XU Qingyu

1. 1.School of Data Science and Technology, North University of China, Taiyuan 030051, China
2.Department of Simulation Equipment, North Automatic Control Technology Institute, Taiyuan 030006, China
• Online:2021-10-15 Published:2021-10-21

### 深度强化学习算法在智能军事决策中的应用

1. 1.中北大学 大数据学院，太原 030051
2.北方自动控制技术研究所 仿真装备部，太原 030006

Abstract:

Deep reinforcement learning algorithm can well achieve discrete decision-making behavior, but it is difficult to apply to the highly complex and continuous modern battlefield situations, and the algorithm is difficult to converge in multi-agent environment. To solve these problems, an improved Deep Deterministic Policy Gradient（DDPG） algorithm is proposed, which introduces the experience replay technology based on priority and single training mode to improve the convergence speed of the algorithm; at the same time, an exploration strategy of mixed double noise is designed in the algorithm to realize complex and continuous military decision-making and control behavior. The intelligent military decision simulation platform based on the improved DDPG algorithm is developed by unity3D. The simulation environment of Blue Army Infantry attacking Red Army military base is built to simulate multi-agent combat training. The experimental results show that the algorithm can drive multiple combat agents to complete tactical maneuvers and achieve tactical behaviors, such as bypassing obstacles to reach the dominant area for shooting. The algorithm has faster convergence speed and better stability. It can get higher round rewards, and achieves the purpose of improving the efficiency of intelligent military decision-making.