计算机科学
强化学习
自动化
步兵
背景(考古学)
人工智能
模拟
人机交互
计算机安全
工程类
政治学
机械工程
生物
古生物学
法学
作者
Minwoo Choi,Hoseok Moon,Sangwoo Han,Yongchan Choi,Minho Lee,Namsuk Cho
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:10: 128970-128982
被引量:1
标识
DOI:10.1109/access.2022.3227797
摘要
Wargame is an important tool that enables training units to develop various strategies by allowing them to experience unexpected situations. There are three methodologies that determine the behavior of the Computer Generated Forces(CGF) in wargame—rule-based, agent-based, and learning-based methodologies. The military determines the behaviors of the CGF mainly based on the rules because a doctrine and an operation plan are well established. However, the advent of intelligent weapons and the accompanying changes in tactics will make it difficult to expect an environment and situations of the future battlefield. Therefore, we studied the automation of CGF through reinforcement learning in order to give unexpected situations, so that the training unit would be able to establish various strategies and tactics through the wargame model. Based on the combat functions of the ground forces, we configured multiple environments that the ground forces CGFs will learn in. First, infantry and artillery CGFs learned in the close combat environment, which is the basis of ground forces combat. Second, the trainee CGF learned in the context of military training. Third, the drone CGF learned how to reconnaissance and attack in a multi-drone environment, and finally, the combat service support CGF learned under the mission of supplying ammunition. As a result, we confirmed that the reinforcement learning methodology is applicable to CGF through these experiments.
科研通智能强力驱动
Strongly Powered by AbleSci AI