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

被引：0

作者：

Shi W. ^{[1
]}

Feng Y.-H. ^{[1
]}

Cheng G.-Q. ^{[1
]}

Huang H.-L. ^{[1
]}

Huang J.-C. ^{[1
]}

Liu Z. ^{[1
]}

He W. ^{[2
,3
]}

机构：

[1] College of Systems Engineering, National University of Defense Technology, Changsha

[2] Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing

[3] School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing

来源：

Feng, Yang-He (fengyanghe@nudt.edu.cn) | 1610年 / Science Press卷 / 47期

基金：

中国国家自然科学基金;

关键词：

Deep reinforcement learning; Enhancement mechanism; Intelligent decision; Multi-aircraft cooperative air combat; Proximal policy optimization (PPO) algorithm;

D O I：

10.16383/j.aas.c201059

中图分类号：

学科分类号：

摘要：

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. Copyright © 2021 Acta Automatica Sinica. All rights reserved.

引用

页码：1610 / 1623

页数：13

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