基于多智能体深度强化学习的分布式协同干扰功率分配算法

饶宁; 许华; 蒋磊; 宋佰霖; 史蕴豪

doi:10.12263/DZXB.20210818

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基于多智能体深度强化学习的分布式协同干扰功率分配算法

电磁频谱智能+ | 更新时间：2025-12-08

- 基于多智能体深度强化学习的分布式协同干扰功率分配算法
- Allocation Algorithm of Distributed Cooperative Jamming Power Based on Multi-Agent Deep Reinforcement Learning
- 电子学报 2022年50卷第6期页码：1319-1330
- 作者机构：
  
  空军工程大学信息与导航学院，陕西西安 710077
- 作者简介：
  
  [ "饶宁男，1997年8月出生，江西上饶人.现为空军工程大学信息与导航学院硕士研究生.主要研究方向为通信对抗、强化学习. E-mail: raoningmabma@163.com" ]
  [ "许华男，1976年4月出生，湖北宜昌人.现为空军工程大学信息与导航学院教授、博士生导师.主要研究方向为通信对抗、信号盲处理.E-mail: 13720720010@139.com" ]
  [ "蒋磊男，1974年6月出生，江苏无锡人.现为空军工程大学信息与导航学院副教授、硕士生导师.主要研究方向为通信对抗、无线通信技术.E-mail: jleimail@126.com" ]
  [ "宋佰霖男，1997年11月出生，辽宁沈阳人.现为空军工程大学信息与导航学院硕士研究生.主要研究方向为通信对抗、强化学习. E-mail: songbail@126.com" ]
  [ "史蕴豪男，1996年7月出生，陕西咸阳人.现为空军工程大学信息与导航学院博士研究生.主要研究方向为信号识别、深度学习. E-mail: shiyunhaoai@163.com" ]
- 基金信息：
- DOI：10.12263/DZXB.20210818
  中图分类号： TN975;
- 收稿：2021-06-30，
  
  修回：2021-10-10，
  
  纸质出版：2022-06-25
- 稿件说明：
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饶宁,许华,蒋磊等.基于多智能体深度强化学习的分布式协同干扰功率分配算法[J].电子学报,2022,50(06):1319-1330.

RAO Ning,XU Hua,JIANG Lei,et al.Allocation Algorithm of Distributed Cooperative Jamming Power Based on Multi-Agent Deep Reinforcement Learning[J].ACTA ELECTRONICA SINICA,2022,50(06):1319-1330.
饶宁,许华,蒋磊等.基于多智能体深度强化学习的分布式协同干扰功率分配算法[J].电子学报,2022,50(06):1319-1330. DOI： 10.12263/DZXB.20210818.

RAO Ning,XU Hua,JIANG Lei,et al.Allocation Algorithm of Distributed Cooperative Jamming Power Based on Multi-Agent Deep Reinforcement Learning[J].ACTA ELECTRONICA SINICA,2022,50(06):1319-1330. DOI： 10.12263/DZXB.20210818.

摘要

针对战场通信对抗协同干扰中的干扰功率分配难题，本文基于多智能体深度强化学习设计了一种分布式协同干扰功率分配算法.具体地，将通信干扰功率分配问题构建为完全协作的多智能体任务，采用集中式训练、分布式决策的方式缓解多智能体系统环境非平稳、决策维度高的问题，减少智能体之间的通信开销，并加入最大策略熵准则控制各智能体的探索效率，以最大化累积干扰奖励和最大化干扰策略熵为优化目标，加速各智能体间协同策略的学习.仿真结果表明，所提出的分布式算法能有效解决高维协同干扰功率分配难题，相比于已有的集中式分配算法具有学习速度更快、波动性更小等优点，且相同条件下干扰效率可高出集中式算法16.8%.

Abstract

In order to solve the problem of jamming power allocation in battlefield cooperative communication countermeasures

this paper designs a distributed cooperative jamming power allocation method based on multi-agent deep reinforcement learning. Specifically

modeling the communication jamming power allocation as a fully cooperative multi-agent task

then the framework of centralized training and distributed decision-making is adopted to alleviate the characteristic of non-stationary environment and high dimensions in multi-agent system

reducing the communication overhead between agents as well

and introducing the maximum policy entropy criterion to control the exploration efficiency of each agent. Regarding maximizing the cumulative jamming reward and maximizing the entropy of the jamming policy as the optimization goal

then accelerates the learning of cooperative strategies. Simulation results indicate the proposed distributed method can effectively solve the high-dimensional cooperative jamming power allocation problem. Compared with the existing centralized allocation method

it has faster learning speed and less volatility

and the jamming efficiency is 16.8% higher than that of the centralized method under the same conditions.

关键词

Keywords

references

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