基于深度强化学习的多机协同空战方法研究

施伟; 冯旸赫; 程光权; 黄红蓝; 黄金才; 刘忠; 贺威

doi:10.16383/j.aas.c201059

基于深度强化学习的多机协同空战方法研究

doi: 10.16383/j.aas.c201059

施伟^1,,
冯旸赫^1,,
程光权^1,,
黄红蓝^1,,
黄金才^1,,
刘忠^1,,
贺威^{2, 3,}

1.
国防科技大学系统工程学院长沙 410073
2.
北京科技大学人工智能研究院北京 100083
3.
北京科技大学自动化学院北京 100083

基金项目: 国家自然科学基金(71701205, 62073333)资助

详细信息

作者简介:
施伟：国防科技大学系统工程学院硕士研究生. 2019年获得国防科技大学学士学位. 主要研究方向为层次强化学习, 多agent智能规划和多agent深度强化学习. E-mail: shiwei15@nudt.edu.cn

冯旸赫：国防科技大学系统工程学院副教授. 获得国防科技大学硕士、博士学位. 主要研究方向为因果发现与推理, 主动学习和强化学习. 本文通信作者. E-mail: fengyanghe@nudt.edu.cn

程光权：国防科技大学系统工程学院副研究员. 主要研究方向为链路预测. E-mail: cgq299@163.com

黄红蓝：国防科技大学系统工程学院博士研究生. 主要研究方向为主动学习, 元学习. E-mail: huanghonglan17@nudt.edu.cn

黄金才：国防科技大学系统工程学院教授. 主要研究方向为智能调度与控制. E-mail: huangjincai@nudt.edu.cn

刘忠：国防科技大学系统工程学院教授. 主要研究方向为智能规划与决策, 深度强化学习和多智能体系统. E-mail: liuzhong@nudt.edu.cn

贺威：北京科技大学人工智能研究院、自动化学院教授. 主要研究方向为机器人学, 振动控制和智能控制系统. E-mail: weihe@ieee.org

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出版历程
- 收稿日期: 2020-12-24
- 网络出版日期: 2021-05-10
- 刊出日期: 2021-07-27

Research on Multi-aircraft Cooperative Air Combat Method Based on Deep Reinforcement Learning

1.
College of Systems Engineering, National University of Defense Technology, Changsha 410073
2.
Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083
3.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083

Funds: Supported by National Natural Science Foundation of China (71701205, 62073333)

More Information

Author Bio:
SHI Wei　Master student at the College of Systems Engineering, National University of Defense Technology. He received his bachelor degree from National University of Defense Technology in 2019. His research interest covers hierarchical reinforcement learning, multi-agent intelligence planning, and multi-agent deep reinforcement learning

FENG Yang-He　Associate professor at the College of Systems Engineering, National University of Defense Technology. He received his master and Ph. D. degrees from National University of Defense Technology. His research interest covers the casual discovery and inference, active learning, and reinforcement learning. Corresponding author of this paper

CHENG Guang-Quan　Associate research fellow at the College of Systems Engineering, National University of Defense Technology. His main research interest is link prediction

HUANG Hong-Lan　Ph. D. candidate at the College of Systems Engineering, National University of Defense Technology. Her research interest covers active learning and meta learning

HUANG Jin-Cai　Professor at the College of Systems Engineering, National University of Defense Technology. His main research interest is intelligent scheduling and control

LIU Zhong　Professor at the College of Systems Engineering, National University of Defense Technology. His research interest covers intelligent planning and decision-making, deep reinforcement learning, and multi-agent system

HE Wei　Professor at the Institute of Artificial Intelligence and School of Automation and Electrical Engineering, University of Science and Technology Beijing. His research interest covers robotics, vibration control, and intelligent control system

摘要

摘要:
多机协同是空中作战的关键环节, 如何处理多实体间复杂的协作关系、实现多机协同空战的智能决策是亟待解决的问题. 为此, 提出基于深度强化学习的多机协同空战决策流程框架(Deep-reinforcement-learning-based multi-aircraft cooperative air combat decision framework, DRL-MACACDF), 并针对近端策略优化(Proximal policy optimization, PPO)算法, 设计4种算法增强机制, 提高多机协同对抗场景下智能体间的协同程度. 在兵棋推演平台上进行的仿真实验, 验证了该方法的可行性和实用性, 并对对抗过程数据进行了可解释性复盘分析, 研讨了强化学习与传统兵棋推演结合的交叉研究方向.
- 多机协同空战 /
- 智能决策 /
- 深度强化学习 /
- PPO算法 /
- 增强机制
Abstract:
Multi-aircraft cooperation is the key part of air combat, and how to deal with the complex cooperation relationship between multi-entities is the essential problem to be solved urgently. In order to solve the problem of intelligent decision-making in multi-aircraft cooperative air combat, a deep-reinforcement-learning-based multi-aircraft cooperative air combat decision framework (DRL-MACACDF) is proposed in this paper. Based on proximal policy optimization (PPO), four algorithm enhancement mechanisms are designed to improve the synergistic degree of agents in multi-aircraft cooperative confrontation scenarios. The feasibility and practicability of the method are verified by the simulation on the wargame platform, and the interpretable review analysis of the antagonistic process data is carried out, and the cross research direction of the combination of reinforcement learning and traditional wargame deduction is discussed.
- Multi-aircraft cooperative air combat /
- intelligent decision /
- deep reinforcement learning /
- proximal policy optimization (PPO) algorithm /
- enhancement mechanism
注释:

1) ¹版本号: v1.4.1.0

HTML全文

图 1 PPO训练流程图

Fig. 1 PPO algorithm training flow chart

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图 2 多机协同空战决策流程框架

Fig. 2 Multi-aircraft collaborative air combat decision framework

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图 3 集中式训练−分布式执行架构

Fig. 3 Framework of centralized training and decentralized execution

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图 4 想定示意图

Fig. 4 Scenario diagram

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图 5 算法有效性对比图

Fig. 5 Algorithm effectiveness comparison diagram

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图 6 消融实验算法性能对比图

Fig. 6 Performance comparison diagram of ablation experimental algorithm

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图 7 累计胜率曲线

Fig. 7 Cumulative winning rate curve

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图 8 胜率分布图

Fig. 8 Winning rate distribution map

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图 9 双机编队

Fig. 9 Two-plane formation

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图 10 三机编队

Fig. 10 Three-plane formation

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图 11 包夹战术

Fig. 11 Converging attack

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图 12 发挥射程优势

Fig. 12 Usage of maximum attack range

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图 13 快速机动避弹

Fig. 13 Fast maneuvers to avoid attack

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图 14 诱骗敌方弹药

Fig. 14 Consume enemy ammunition

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A1 神经网络示意图

A1 Diagrams of neural network

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表 1 算法有效性实验数据统计

Table 1 Experimental statistics of algorithm effectiveness

算法	平均得分	得分标准差	平均胜率 (%)
DRL-MACACDF	18.929	10.835	91.472
PPO	−21.179	1.698	0

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表 2 消融实验设置

Table 2 The setting of ablation experiment

模型	嵌入式专家经验奖励机制	经验共享机制	自适应权重及优先采样机制	鼓励探索机制
DRL-MACACDF	●	●	●	●
DRL-MACACDF-R	○	●	●	●
DRL-MACACDF-A	●	○	●	●
DRL-MACACDF-S	●	●	○	●
DRL-MACACDF-E	●	●	●	○
注: ● 表示包含该机制, ○ 表示不包含

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表 3 消融实验数据统计

Table 3 Statistics of ablation experimental results

模型	平均得分	平均得分比传统 PPO 提高百分比 (%)	平均胜率 (%)
RL-MACACDF-R	−19.297130	8.327	0
RL-MACACDF-A	13.629237	154.019	86.774
RL-MACACDF-S	5.021890	115.934	66.673
RL-MACACDF-E	8.973194	133.417	82.361

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A1 实验超参数设置

A1 Experimental hyperparameter setting

参数名	参数值	参数名	参数值
网络优化器	Adam	经验库容量	3000 (个)
学习率	5 × 10⁻⁵	批大小	200 (个)
折扣率	0.9	$ \tau $初始值	1.0
裁剪率	0.2	${\tau _{{\rm{step}}} }$	1 × 10⁻⁴
训练开始样本数	1400 (个)	${\tau _{{\rm{temp}}} }$	50000

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A2 想定实体类型

A2 Entity type of scenario

单元类型	数量	主要作战武器
F/A-18 型战斗机	2	4 × AIM-120D 空空导弹 2 × AGM-154C 空地导弹
F-35C 型战斗机	1	6 × AGM-154C 空地导弹
基地	1	2 × F/A-18 型战斗机 1 × F-35C 型战斗机

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A3 推演事件得分

A3 The score of deduction events

推演事件	得分
击毁一架飞机	139
损失一架飞机	−139
击毁基地	1843
损失基地	−1843

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A4 状态空间信息

A4 State space information

实体	信息
己方飞机	经度、纬度、速度、朝向、海拔、目标点经度、目标点纬度等 7 维信息
己方导弹	经度、纬度、速度、朝向、海拔、打击目标的经度、打击目标的纬度等 7 维信息
敌方飞机	经度、纬度、速度、朝向、海拔等 5 维信息
敌方导弹	经度、纬度、速度、朝向、海拔等 5 维信息

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A5 动作空间信息

A5 Action space information

类别	取值范围
飞行航向	0°、60°、120°、180°、240°、300°
飞行高度	7620 米、10973 米、15240 米
飞行速度	低速、巡航、加力
自动开火距离	35 海里、40 海里、45 海里、 50 海里、60 海里、70 海里
导弹齐射数量	1 枚、2 枚

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