A Meta-reinforcement Learning Approach for Cooperative Decision-making of Multi-interceptors in Adversarial Environments

SANG Jimao; GUO Yang; YU Chuanqiang; LIU Jia; ZHANG Chengyi

doi:10.12382/bgxb.2025.0771

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A Meta-reinforcement Learning Approach for Cooperative Decision-making of Multi-interceptors in Adversarial Environments

更新时间：2026-04-24

- A Meta-reinforcement Learning Approach for Cooperative Decision-making of Multi-interceptors in Adversarial Environments
- Acta Armamentarii Vol. 47, Issue 1, Pages: 250771(2026)
- 作者机构：
  
  火箭军工程大学，陕西西安 710025
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0771
  CLC： TJ765.3;TP181
- Received：25 August 2025，
  
  Online First：11 February 2026，
  
  Published：31 January 2026
- 稿件说明：
移动端阅览
SANG Jimao, GUO Yang, YU Chuanqiang, et al. A Meta-reinforcement Learning Approach for Cooperative Decision-making of Multi-interceptors in Adversarial Environments[J]. Acta Armamentarii, 2026, 47(1): 250771.
DOI：

SANG Jimao, GUO Yang, YU Chuanqiang, et al. A Meta-reinforcement Learning Approach for Cooperative Decision-making of Multi-interceptors in Adversarial Environments[J]. Acta Armamentarii, 2026, 47(1): 250771. DOI： 10.12382/bgxb.2025.0771.

摘要

针对强对抗环境下高机动目标不可预测机动与电磁干扰导致的多拦截器动态目标分配环境适应性差、训练效率低等问题，提出一种自适应元强化学习目标分配方法。该方法融合元学习与改进型A3C算法，构建双模态协同决策机制。通过可微分元知识库实现跨任务策略迁移与快速环境适配；基于威胁等级引入渐进式课程学习，分阶段调节环境复杂度，增强算法对突发干扰与目标机动突变的鲁棒性；采用异构智能体分层参数共享、延迟感知梯度同步及双重优先级经验回放，提升分布式策略训练的样本效率与收敛稳定性。仿真结果表明，新方法显著提高了拦截成功率与协同决策实时性，有效克服了传统博弈论方法动态适应性差、计算复杂度过高，以及深度强化学习在部分可观测下样本效率低、突发干扰鲁棒性不足的局限，为复杂对抗场景下的多智能体协同分配与制导提供了有效支撑。

Abstract

The unpredictable maneuvers of highly mobile targets and the electromagnetic interference result in the poor environmental adaptability and low training efficiency in multi-interceptor dynamic target allocation under strong adversarial conditions. This paper proposes an adaptive meta-reinforcement learning target allocation framework. This framework integrates meta-learning with an improved asynchronous advantage actor-critic (A3C ) algorithm to establish a dual-mode collaborative decision-making mechanism. Firstly

the cross-task strategy transfer and rapid environmental adaptation are achieved through a differentiable meta-knowledge base. Subsequently

a progressive curriculum learning is introduced based on threat levels

enabling the phased adjustment of environmental complexity to enhance the algorithm's robustness against sudden interference and abrupt target maneuvering. And then the heterogeneous agent hierarchical parameter sharing

delay-aware gradient synchronization and dual-priority experience replay are employed to improve the sample efficiency and convergence stability of distributed strategy training. Simulated results demonstrate that the proposed method increases the interception success rates and enhances the real-time performance of collaborative decision-making. It effectively overcomes the limitations

such as poor dynamic adaptability and excessively high computational complexity

of traditional game-theoretic methods

as well as the shortcomings

including low sample efficiency and insufficient robustness to sudden interference

of deep reinforcement learning in partially observable environments. This work provides effective support for multi-agent collaborative allocation and guidance in complex adversarial scenarios.

关键词

Keywords

references

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