.png){kind=icon}
返回
Learning-Based Policy Optimization for Adversarial Missile-Target Assignment
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2021.3096997.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The missile-target assignment (MTA) is a typical weapon-target assignment problem in Command and Control of modern warfare. Despite the significance of the problem, traditional algorithms still lack efficiency, solution quality, and practicability in the adversarial environment. In this article, we propose a data-driven policy optimization with deep reinforcement learning (PODRL) for the adversarial MTA. We design a comprehensive reward function to motivate the optimization of assignment policy. As such, the learned policy can implicitly model the penetration of missiles under an adversarial environment in a data-driven way. We also present a fair sample strategy to improve the sample efficiency and accelerate the policy optimization. Experimental results show that PODRL can adaptively generate satisfactory solutions in both small-scale and large-scale instances. Furthermore, we evaluate the effectiveness of PODRL in a multiobjective scenario. The result demonstrates that a well-optimized policy can achieve high-quality allocation and demand forecast of the missile resources simultaneously.
Keyword:
Missiles
Optimization
Search problems
Discrete wavelet transforms
Resource management
Heuristic algorithms
Mathematical model
Adversarial environment
deep Q-learning with fair sample
deep reinforcement learning (DRL)
missile-target assignment (MTA)
policy optimization
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏Weapon-Target Assignment for Multi-to-Multi Interception With Grouping Constraint.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容