多智能体强化学习框架下机器人集群通信技术综述

盖思博; 马蓓; 郑莉; 苏波

doi:10.12382/bgxb.2025.0603

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多智能体强化学习框架下机器人集群通信技术综述

更新时间：2026-04-24

- 多智能体强化学习框架下机器人集群通信技术综述
- A Survey of Communication Technology for MARL-based Robot Swarm
- 兵工学报 2026年47卷第1期页码：250603
- 作者机构：
  
  群体协同与自主国家级重点实验室，北京 100072
  中兵智能创新研究院有限公司，北京 100072
- 作者简介：
  
  *通信作者邮箱：zhengli1217@163.com
- 基金信息：
  
  国家自然科学基金项目(92248303)
- DOI：10.12382/bgxb.2025.0603
  中图分类号： TP181
- 收稿：2025-07-08，
  
  网络首发：2026-02-11，
  
  纸质出版：2026-01-31
- 稿件说明：
移动端阅览
盖思博, 马蓓, 郑莉, 等. 多智能体强化学习框架下机器人集群通信技术综述[J]. 兵工学报, 2026,47(1):250603.

GAI Sibo, MA Bei, ZHENG Li, et al. A Survey of Communication Technology for MARL-based Robot Swarm[J]. Acta Armamentarii, 2026, 47(1): 250603.
盖思博, 马蓓, 郑莉, 等. 多智能体强化学习框架下机器人集群通信技术综述[J]. 兵工学报, 2026,47(1):250603. DOI： 10.12382/bgxb.2025.0603.

GAI Sibo, MA Bei, ZHENG Li, et al. A Survey of Communication Technology for MARL-based Robot Swarm[J]. Acta Armamentarii, 2026, 47(1): 250603. DOI： 10.12382/bgxb.2025.0603.

摘要

多智能体强化学习是解决机器人集群控制、决策和探索的重要技术，是实现大规模无人自动化以及迈向通用智能的重要方法，在军事、交通以及物流等方面都发挥着重要的作用。在多智能体强化学习中，通信扮演了关键角色，近年来已经成为该方向的关注热点。针对此研究问题，归纳总结了该方向近年来的主要研究成果，对多智能体强化学习机器人集群中的通信问题所关心的主要研究方向进行了分类总结，整理并阐述了机器人集群通信中的核心研究机制和其他强化学习相关的专门方向，讨论了各个问题的主要研究对象和技术路线，并提出了目前该领域所面临的挑战、亟需解决的关键问题以及未来的发展方向。

Abstract

Multi-agent reinforcement learning (MARL) serves as a pivotal technology for addressing the control

decision-making and exploration of robot swarm. It represents a critical approach toward achieving large-scale unmanned automation and moving towards general artificial intelligence

with significant applications in military

transportation

and logistics

etc. Communication plays a central role in MARL and has emerged as a key research focus in recent years. The main research advancements in this field over the past few years are systematically reviewed

and the primary research directions concerning the communication for MARL-based robot swarms are categorized and summarized. It elaborates on the core research mechanisms with the MARL and the specialized subfields related to reinforcement learning

discusses the principal research objectives and technical approaches for various communication-related challenges

and identifies the current challenges in this field

the critical issues that need to be addressed urgently

and the prospective future developments.

关键词

Keywords

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