A Sequence-to-sequence Deep Neural Network-based Optimization Method for the Weapon-target Assignment

GUO Yanbin; FANG Feng; WANG Zhenya; ZUO Yan; FAN Chunhao; LI Wentao

doi:10.12382/bgxb.2025.0915

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A Sequence-to-sequence Deep Neural Network-based Optimization Method for the Weapon-target Assignment

更新时间：2026-07-06

- A Sequence-to-sequence Deep Neural Network-based Optimization Method for the Weapon-target Assignment
- Acta Armamentarii Vol. 47, Issue 6, Pages: 250915(2026)
- 作者机构：
  
  杭州电子科技大学自动化学院，浙江杭州 310018
  中国航天科技体系与创新研究院算法中心，北京 100088
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0915
  CLC： E91
- Received：13 October 2025，
  
  Online First：13 February 2026，
  
  Published：2026-06
- 稿件说明：
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GUO Yanbin, FANG Feng, WANG Zhenya, et al. A Sequence-to-sequence Deep Neural Network-based Optimization Method for the Weapon-target Assignment[J]. Acta Armamentarii, 2026, 47(6): 250915.
DOI：

GUO Yanbin, FANG Feng, WANG Zhenya, et al. A Sequence-to-sequence Deep Neural Network-based Optimization Method for the Weapon-target Assignment[J]. Acta Armamentarii, 2026, 47(6): 250915. DOI： 10.12382/bgxb.2025.0915.

摘要

考虑大规模武器-目标分配（Weapon-Target Assignment，WTA）面临决策空间复杂度高、求解效率低的问题，综合应用指针网络、Actor-Critic策略梯度更新机制、预训练模型在线学习微调等方法，提出一种改进序列到序列深度神经网络的WTA优化求解方法，提升了优化求解的质量、速度和一定泛化性。将WTA优化决策构建为武器-目标信息序列到武器-目标打击配对序列的映射问题，建立基于长短期记忆网络为编码器和解码器的指针网络模型，引入交叉注意力机制实现目标与武器的动态匹配，降低输入次序变化对决策性能的影响。采用Actor-Critic方式更新模型梯度加速网络学习速度，减小REINFORCE策略梯度更新的方差。建立离线预训练模型与在线微调相结合的两阶段优化机制，提升了多样化场景下的决策泛化性。仿真结果表明：所提方法相较于传统启发式算法，具有可兼顾求解质量与求解速度的优点；相较于深度Q网络为代表的深度强化学习优化方法，具有更好的泛化性和鲁棒性。

Abstract

Considering the high complexity of decision-making space and the low efficiency of traditional solution methods in large-scale weapon-target assignment (WTA )

this paper proposes an improved sequence-to-sequence deep neural network approach that integrates pointer networks

Actor-Critic policy gradient update mechanism

and online fine-tuning of pre-trained models. The proposed method improves the solution quality

computational efficiency and generalization capability. The WTA optimization problem is formulated as a mapping from the weapon-target information sequences to the weapon-target engagement assignment sequences. A pointer network model is constructed using long short-term memory (LSTM) networks as the encoder and decoder

and a cross-attention mechanism is used to dynamically model the weapon-target interactions

thereby reducing the impact of variations in input ordering on decision-making performance. The model gradients are updated using the Actor-Critic framework to accelerate the learning speed of the network and reduce the variance of gradient updates in REINFORCE-based strategy.Furthermore

a two-stage optimization mechanism that combines the offline pre-training with the online fine-tuning is developed to improve the generalization capability of the algorithm across diverse operational scenarios. Simulated results show that the proposed approach achieves a superior balance between solution quality and computational speed compared with traditional heuristic algorithms. In addition

it has stronger generalization capability and robustness compared with the deep reinforcement learning approaches such as deep Q-network.

关键词

Keywords

references

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