A Survey of Communication Technology for MARL-based Robot Swarm

GAI Sibo; MA Bei; ZHENG Li; SU Bo

doi:10.12382/bgxb.2025.0603

您当前的位置：

首页 >

文章列表页 >

A Survey of Communication Technology for MARL-based Robot Swarm

更新时间：2026-04-24

- A Survey of Communication Technology for MARL-based Robot Swarm
- Acta Armamentarii Vol. 47, Issue 1, Pages: 250603(2026)
- 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：
- 基金信息：
- DOI：10.12382/bgxb.2025.0603
  CLC： TP181
- Received：08 July 2025，
  
  Online First：11 February 2026，
  
  Published：31 January 2026
- 稿件说明：
移动端阅览
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：

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

references

NING Z P, XIE L H. A survey on multi-agent reinforcement learning and its application [J]. Journal of Automation and Intelligence,2024,3（2）:73-91.

LI Z Y, ZHAO W S, PAJARINEN J. Cooperative multi-agent planning with adaptive skill synthe-sis: arXiv:2502. 10148 [R]. Ithaca, NY, US:Cornell University,2025:2502. 10148.

ZHU C X, DASTANI M, WANG S H. A survey of multi-agent deep reinforcement learning with communication [J]. Autonomous Agents and Multi-Agent Systems,2024,38（1）:4.

王涵，俞扬，姜远.基于通信的多智能体强化学习进展综述[J].中国科学：信息科学，2022,52（5）:742-764.

WANG H, YU Y, JIANG Y.Review of the progress of communication-based multi-agent reinforcement learning[J].Scientia Sinica（Informationis）,2022,52（5）:742-764.（in Chinese）

ZHU S H, ZHOU J C, CHEN A J, et al. MAexp:a generic platform for RL-based multi-agent exploration [C]∥Proceedings of the IEEE International Conference on Robotics and Automation. Yokoham, Japan:IEEE,2024:5155-5161.

孔国杰，冯时，于会龙，等.无人集群系统协同运动规划技术综述[J].兵工学报，2023,44（1）:11-26.

KONG G J, FENG S, YU H L, et al.A review on coopera-tive motion planning of unmanned vehicles[J].Acta Armamentarii, 2023,44（1）:11-26.（in Chinese）

YANG E F, GU D B. Multiagent reinforcement learning for multi-robot systems: a survey [Z]. Colchester, UK: University of Essex,2004.

GODOY J E, KARAMOUZAS I, GUY S J, et al. Adaptive learning for multi-agent navigation [C]∥Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems. Richland, SC, US: International Foundation for Autonomous Agents and Multiagent Systems,2015:1577-1585.

SUKHBAATAR S, SZLAM A, FERGUS R. Learning multiagent communication with backpropagation: arXiv: 1605. 07736 [R]. Ithaca, NY, US:Cornell University,2016:1605. 07736.

FOERSTER J N, ASSAEL Y M, DE FREITAS N, et al. Learning to communicate with deep multi-agent reinforcement learning:arXiv:1605. 06676 [R]. Ithaca, NY, US: Cornell University, 2016:1605. 06676.

NACHUM O, AHN M, PONTE H, et al. Multi-agent manipulation via locomotion using hierarchical Sim2Real[C]∥Proceedings of the 3rd Annual Conference on Robot Learning. Osaka, Japan:PMLR,2019,100:110-121.

FERNANDEZ-GAUNA B, ETXEBERRIA-AGIRIANO I, GRAÑA M. Learning multirobot hose transportation and deployment by distributed round-robin Q-learning [J]. PloS One, 2015, 10（7）:e0127129.

NIROUI F, ZHANG K, KASHINO Z, et al. Deep reinforcement learning robot for search and rescue applications: exploration in unknown cluttered environments [J]. IEEE Robotics and Automation Letters,2019,4（2）:610-617.

XIONG Z Y, CHEN B, HUANG S Y, et al. MQE:unleash-ing the power of interaction with multi-agent quadruped environment[C]∥Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Abu Dhabi, United Arab Emirates:IEEE,2024:5918-5924.

LI Z X, SHI N F, ZHAO L G, et al. Deep reinforcement learning path planning and task allocation for multi-robot collaboration [J].Alexandria Engineering Journal,2024,109:408-423.

NAHRENDRA I M A, YU B, MYUNG H. DreamWaQ: learning robust quadrupedal locomotion with implicit ter-rain imagination via deep reinforcement learning[C]∥Proceedings of the IEEE International Conference on Robotics and Automation. London, UK:IEEE,2023:5078-5084.

PANDIT B, GUPTA A, GADDE M S, et al. Learning decentralized multi-biped control for payload transport [C]∥AGRAWAL P, KROEMER O, BURGARD W. Proceedings of Conference on Robot Learning. Munich, Germany:PMLR,2024, 270:1021-1034.

CHEN W T, NGUYEN M, LI Z Y, et al. Decentralized navigation of a cable-towed load using quadrupedal robot team via MARL:arXiv:2503. 18221 [R]. Ithaca, NY, US: Cornell University, 2025:2503. 18221.

BETRAN S B, LONGHINI A, VASCO M, et al. FLAME: a federated learning benchmark for robotic manipultion: arXiv:2503. 01729 [R]. Ithaca, NY, US: Cornell University, 2025:2503. 01729.

GAO Q, ZHONG R K, SHIN H D, et al. MARL-based UAV trajectory and beamforming optimization for ISAC system [J]. IEEE Internet of Things Journal,2024,11（24）:40492-40505.

YADAV P, MISHRA A, KIM S. A comprehensive survey on multi-agent reinforcement learning for connected and automated vehicles[J]. Sensors,2023,23（10）:4710.

丁世飞，杜威，张健，等.多智能体深度强化学习研究进展[J].计算机学报，2024,47（7）:1547-1567.

DING S F, DU W, ZHANG J, et al.Research progress of multi-agent deep reinforcement learning [J].Chinese Journal of Computers,2024,47（7）:1547-1567.（in Chinese）

XIE S R, LI Y, WANG X Z, et al. Hierarchical relationship modeling in multi-agent reinforcement learning for mixed cooperative-competitive environments [J]. Information Fusion, 2024,108:102318.

王军，曹雷，陈希亮，等.多智能体博弈强化学习研究综述[J].计算机工程与应用，2021,57（21）:1-13.

WANG J, CAO L, CHEN X L, et al.Overview on reinforcement learning of multi-agent game [J].Computer Engineering and Applications,2021,57（21）:1-13.（in Chinese）

SHEN G C, WANG Y. Review on Dec-POMDP model for MARL algorithms [C]∥Proceedings of the Smart Communications, Intelligent Algorithms and Interactive Methods, Proceedings of 4th International Conference on Wireless Communications and Applications. Singapore:Springer,2022:29-35.

OROOJLOOV A, HAJINEZHAD D. A review of cooperative multi-agent deep reinforcement learning [J]. Applied Intelligence, 2023,53（11）:13677-13722.

GUERTRIN C G, LAGOUDAKIS M, PARR R. Coordinated reinforcement learning [C]∥Proceedings of the International Conference on Machine Learning. Sydney, Australia: Morgan Kaufmann Publishers Inc. ,2002,2:227-234.

GUPTA J K, EGOROV M, KOCHENDERFEI M. Cooperative multi-agent control using deep reinforcement learning [C]∥Proceedings of the Autonomous Agents and Multiagent Systems:AAMAS 2017 Workshops. São Paulo, Brazil: Springer, 2017:66-83.

LIU S Q, XING D, GU P J, et al. Solving homogeneous and heterogeneous cooperative tasks with greedy sequential execution [C]∥Proceedings of the Twelfth International Conference on Learning Representations. Vienna, Austria: IEEE Information Theory Society,2024.

FENG P, LIANG J K, WANG S Z, et al. Hierarchical consensus-based multi-agent reinforcement learning for multi-robot cooperation tasks[C]∥Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Abu Dhabi, UAE:IEEE,2024:642-649.

JIA Y J, SONG Y, XIONG B, et al. Hierarchical perceptionimproving for decentralized multi-robot motion planning in complex scenarios [J]. IEEE Transactions on Intelligent Transportation Systems,2024,25（7）:6486-6500.

NASH J. Equilibrium points in n-person games[J]. Proceedings of the National Academy of Sciences,1950,36（1）:48-49.

DENG X T, LI N Y, MGUNI D, et al. On the complexity of computing markov perfect equilibrium in general-sum stochastic games[J].National Science Review,2023,10（1）:nwac256.

CHEN J X, XIE W J, ZHANG W N, et al. Offline fictitious selfplay for competitive games:arXiv:2403. 00841 [R]. Ithaca, NY, US:Cornell University,2024:2403. 00841.

HU G Z, ZHU Y H, LI H R, et al. FM3Q:factorized multi-agent MiniMax Q-learning for two-team zero-sum Markov game [J]. IEEE Transactions on Emerging Topics in Computational Intelligence,2024,8（6）:4033-4045.

VINYALS O, BABUSCHKIN I, CZARNECKI W M, et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning[J]. Nature,2019,575（7782）:350-354.

CHEN J Y, XU Z L, LI Y F, et al. Accelerate multi-agent reinforcement learning in zero-sum games with subgame curriculum learning: arXiv: 2310. 04796 [R]. Ithaca, NY, US:Cornell University,2023:2310. 04796.

PENG P, WEN Y, YANG Y D, et al. Multiagent bidirectionallycoordinated nets: Emergence of human-level coordination in learning to play starcraft combat games:arXiv:1703.10069[R]. Ithaca, NY, US:Cornell University,2017:1703. 10069.

YANG Y D, LUO R, LI M N, et al. Mean field multi-agent reinforcement learning:arXiv:1802.05438[R]. Ithaca, NY, US:Cornell University,2018:1802. 05438.

GAO Y, CHEN J F, CHEN X, et al. Asymmetric self-play-enabled intelligent heterogeneous multirobot catching system using deep multiagent reinforcement learning [J]. IEEE Transactions on Robotics,2023,39（4）:2603-2622.

HU Z H, LI X X, MENG M, et al. A game theoretic method for two-team multi-player autonomous racing[J]. IEEE Robotics and Automation Letters,2024,9（9）:7581-7588.

WANG J H, LI Y, ZHANG Y, et al. Open ad hoc teamwork with cooperative game theory [C]∥ Proceedings of the 41st International Conference on Machine Learning. Vienna, Austria:JMLR. org,2024,235:50902-50930.

CHEN X, DENG X T, TENG S H. Settling the complexity of two-player nash equilibrium:arXiv:0704.1678[R].Ithaca, NY, US:Cornell University,2007:0704. 1678.

ZINKEVICH M, GREENWALD A, LITTMAN M. Cyclic equilibria in Markov games[C]∥Proceedings of Advances in Neural Information Processing Systems. Vancouver, BC, Canada:MIT Press,2005.

BANERIEE S, ULUKUS S. The freshness game: timely communications in the presence of an adversary[J]. IEEE/ACM Transactions on Networking,2024,32（5）:4067-4084.

MA Y D, LIUK, LIU Y M, et al. An intelligent game-based antijamming solution using adversarial populations for aerial communication networks [J]. IEEE Transactions on Cognitive Communications and Networking,2025,11（3）:1981-1995.

JIANG J C, LU Z Q. Learning attentional communication for multi-agent cooperation:arXiv:1805. 07733[R]. Ithaca, NY, US:Cornell University,2018:1805. 07733.

SINGH A, JAIN T, SUKHBAARTAR S. Learning when to communicate at scale in multiagent cooperative and competitive tasks: arXiv: 1812. 09755 [R]. Ithaca, NY, US: Cornell University,2018:1812. 09755.

JIA Y J, SONG Y, CHENG J Y, et al. A deep reinforcement learning approach using asymmetric self-play for robust multirobot flocking[J]. IEEE Transactions on Industrial Informatics,2025, 21（4）:3266-3275.

XIONG M L, XIE G M. Swarm game and task allocation for autonomous underwater robots[J]. Journal of Marine Science and Engineering,2023,11（1）:148.

SU Z, GAO Y M, LUKAS E, et al. oward real-world cooperative and competitive soccer with quadrupedal robot teams: arXiv:2505. 13834 [R]. Ithaca, NY, US: Cornell University, 2025:2505. 13834.

GUPTA N, SRINIVASARAGHAVAN G, MOHALIK S, et al. Hammer:multi-level coordination of reinforcement learning agents via learned messaging[J]. Neural Computing and Applications, 2025,37:13221-13236.

BA Y W, LIU X, CHEN X N, et al. Cautiously-optimistic knowledge sharing for cooperative multi-agent reinforcment learning: arXiv: 2312. 12095 [R]. Itaca, NY, US: Cornell University,2023:2312. 12095.

CHAI J J, ZHU Y H, ZHAO D B. NVIF:neighboring variational information flow for cooperative large-scale multiagent reinforcement learning [J]. IEEE Transactions on Neural Networks and Learning Systems,2024,35（12）:17829-17841.

ZHU T Y, SHI X L, XU X P, et al. HyperComm: hypergraph-based communication in multi-agent reinforcement learning[J]. Neural Networks,2024,178:106432.

YUAN L, WANG J H, ZHANG F X, et al. Multi-agent incentive communication via decentralized teammate modeling [C]∥Proceedings of the Thirty-sixth AAAI Conference on Artificial Intelligence, Thirty-fourth Conference on Innovative Applications of Artificial Intelligence, the Twelveth Symposium on Educational Advances in Artificial Intelligence. Menlo Park, CA, US: AAAI Press,2022:9466-9474.

ZHOU L, DENG X F, WANG Z, et al. Semantic information extraction and multi-agent communication optimization based on generative pre-trained transformer [J]. IEEE Transactions on Cognitive Communications and Networking,2025,11（2）:725-737.

LIANG Q Y, LIU J, JIANG Z M, et al. Limited information aggregation for collaborative driving in multi-agent autonomous vehicles[J]. IEEE Robotics and Automation Letters,2024,9:6624-6631.

ZHOU X H, XIONG J, ZHAO H T, et al. Joint UAV trajectory and communication design with heterogeneous multi-agent reinforcement learning[J]. Science China Information Sciences, 2024,67（3）:132302.

LIU Z Y, WAN L P, SUI X F, et al. Multi-agent intention sharing via leader-follower forest: arXiv: 2112. 01078 [R]. Ithaca, NY, US:Cornell University,2021:2112. 01078.

XU J, WEI W, ZHANG Y, et al. Partial communication model based on the gain of q-value in multi-agent reinforcement learning [C]∥Proceedings of the 2024 14th Asian Control Conference. Dalian, China:IEEE,2024:69-74.

QIU D W, WANG J H, DONG Z H, et al. Mean-field multi-agent reinforcement learning for peer-to-peer multi-energy trading[J]. IEEE Transactions on Power Systems,2023,38:4853-4866.

ZHONG Y F, KUBA J G, FENG X D, et al. Heterogeneous-agent reinforcement learning:arXiv:2304.09870[R]. Ithaca, NY, US:Cornell University,2023:2304. 09870.

YANG X Y, HUANG S Y, SUN Y W, et al. Learning graphenhanced commander-executor for multi-agent navigation[C]∥Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems. London, UK:ACM,2023:1652-1660.

YAO Z X, XIA S C, LI Y, et al. Cooperative task offloading and service caching for digital twin edge networks: a graph attention multi-agent reinforcement learning approach[J]. IEEE Journal on Selected Areas in Communications,2023,41:3401-3413.

YUAN L, LI L H, ZHANG Z Q, et al. Multiagent continual coordination via progressive task contextualization [J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 36:6326-6340.

JIN D H, ZENG Y, GONG Y. Bandwidth-efficient communication modelling for autonomous vehicle collaborative perception[C]∥Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision. Tucson, AZ, US: IEEE,2025:6146-6155.

PARADA L, YU K, ANGELOUDIS P. IntNet:a communicationdriven multi-agent reinforcement learning framework for cooperative autonomous driving [J].IEEE Robotics and Automation Letters,2025,10（3）:2478-2485.

KIM W J, CHO M, SUNG Y. Message-dropout: an efficient training method for multi-agent deep reinforcement learning[C]∥Proceedings of the Thirty-third AAAI Conference on Artificial Intelligence, the Thirty-first Innovative Applications of Artificial Intelligence Conference, the Ninth AAAI Symposium on Educational Advances in Artificial Intelligence. Honolulu, HI, US:AAI Press,2019:6079-6086.

SHI Z Y. Community-based multi-agent reinforcement learning with transfer and active exploration: arXiv: 2505. 09756 [R]. Ithaca, NY, US:Cornell University,2025:2505. 09756.

WONG A, BÄCK T, KONONOVA A V, et al. Deep multiagent reinforcement learning: challenges and directions [J]. Artificial Intelligence Review,2023,56（6）:5023-5056.

闫超，徐昕，相晓嘉，等.多智能体深度强化学习及其可扩展性与可迁移性研究综述[J].控制与决策，2022,37（12）:3083-3102.

YAN C, XU X, XIANG X X, et al.A survey on scalability and transferability of multi-agent deep reinforcement learn ing[J].Control and Decision,2022,37（12）:3083-3102.（in Chinese）

FENG Y M, HONG C Y, NIU Y R, et al. Learning multi-agent loco-manipulation for long-horizon quadrupedal pushing: arXiv:2411. 07104 [R]. Ithaca, NY, US: Cornell University, 2024:2411. 07104.

HAN L, ZHU Q X, SHENG J P, et al. Lifelike agility and play in quadrupedal robots using reinforcement learning and generative pre-trained models [J]. Nature Machine Intelligence, 2024, 6（7）:787-798.

陈谋，雍可南，马浩翔，等.无人机安全飞行控制综述[J].机器人，2023,45（3）:345-366.

CHEN M, YONG K N, MA H X, et al.Safety flight control of UAV:a survey[J].Robot,2023,45（3）:345-366.（in Chinese）

PANDEY G K, GURJAR D S, NGUYEN H H, et al. Security threats and mitigation techniques in UAV communications: a comprehensive survey [J]. IEEE Access, 2022, 10: 112858-112897.

ZHANG S Q, LIN J Y, ZHANG Q. Succinct and robust multi-agent communication with temporal message control: arXiv:2010. 14391[R]. Ithaca, NY, US: Cornell University, 2020:2010. 14391.

SUN C X, ZANG Z H, LI J B, et al. T2MAC:targeted and trusted multi-agent communication through selective engagement and evidence-driven integration[C]∥Proceedings of the Thirty-eighth AAAI Conference on Artificial Intelligence, Thirty-sixth Conference on Innovative Applications of Artificial Intelligence, Fourteenth Symposium on Educational Advances in Artificial Intelligence. Vancouver, BC, Canada:AAAI Press,2024:15154-15163.

MA Y X, LIANG J S, CAO Y H, et al. Privileged reinforcement and communication learning for distributed, bandwidth-limited multi-robot exploration:arXiv:2407.20203[R].Ithaca, NY, US:Cornell University,2024:2407. 20203.

WANG T H, WANG J H, ZHENG C Y, et al. Learning nearly decomposable value functions via communication minimization:arXiv:1910. 05366 [R]. Ithaca, NY, US: Cornell University, 2019:1910. 05366.

FEMG Z K, WU D, HUANG M X, et al. Event-driven transformer-based reinforcement learning for trajectory design and channel assignment in multi-UAV assisted communication [J]. IEEE Transactions on Cognitive Communications and Networking, 2025,11（6）:4254-4266.

HUA M, CHEN D, JIANG K, et al. Communication-efficient MARL for platoon stability and energy-efficiency co-optimization in cooperative adaptive cruise control of cavs[J]. IEEE Transactions on Vehicular Technology,2025,74（4）:6076-6087.

SHI R Y, YU X, WANG Y D, et al. Symmetry-informed MARL:a decentralized and cooperative UAV swarm control approach for communication coverage [J]. IEEE Transactions on Mobile Computing,2025,24（9）:8039-8056.

LEE D G, SUN Y G, KIM S H, et al. Multi-agent reinforcement learning-based resource allocation scheme for UAV-assisted internet of remote things systems [J]. IEEE Access: Practical Innovations, Open Solutions,2023,11:53155-53164.

FANG Z, JIANG D, HUANG J, et al. Autonomous underwater vehicle formation control and obstacle avoidance using multi-agent generative adversarial imitation learning[J]. Ocean Engineering, 2022,262:112182.

LUO R Y, NI W L, TIAN H. Visualizing multi-agent reinforcement learning for robotic communication in industrial IoT networks[C]∥Proceedings of the IEEE INFOCOM 2022 IEEE Conference on Computer Communications Workshops. New York, NY, US:IEEE,2022:1-2.

AI Q X, HOU C B, ZHOU Z C, et al. KNIGCN: key node identification in UAV swarm networks using a graph convolutional network[J]. IEEE Internet of Things Journal, 2025,12（11）:16227-16242.

SONG Y H, DING G R, SUN J C, et al. Topology tracking of dynamic UAV wireless networks [J]. Chinese Journal of Aeronautics,2022,35（11）:322-335.

WANG J Y, LI X R, GUO J H, et al. Self learning-based platooning control strategy for connected autonomous vehicles with switching topologies [J]. IEEE Transactions on Intelligent Transportation Systems,2024,25（12）:19842-19851.

LIU R K, REN Y H, YU H Y, et al. Connected and automated vehicle platoon maintenance under communication failures [J]. Vehicular Communications,2022,35:100467.

XU Y Q, SHI Y, TIAN J, et al. DCT-MARL: a dynamic communication topology-based MARL algorithm for connected vehicle platoon control: arXiv:2508. 12633v2 [R]. Ithaca, NY, US:Cornell University,2025:2508. 12633v2.

WANG Z W, JIN S F, LIU L H, et al. Design of intelligent connected cruise control with vehicle-to-vehicle communication delays[J]. IEEE Transactions on Vehicular Technology,2022, 71（8）:9011-9025.

YUVILER T, DRACHSLER-COHEN D. One pixel adversarial attacks via sketched programs[J]. Proceedings of the ACM on Programming Languages,2023:1970-1994.

FREED B, SARTORETTI G, HU J H, et al. Communication learning via backpropagation in discrete channels with unknown noise[C]∥Proceedings of the Thirty-fourth AAAI Conference on Artificial Intelligence, the Thirty-second Innovative Applications of Artificial Intelligence Conference, the Tenth AAAI Symposium on Educational Advances in Artificial Intelligence. New York, NY, US:AAAI Press,2020:7160-7168.

TUNG T Y, KOBUS S, ROIG J S P, et al. Effective communications:a joint learning and communication framework for multi-agent reinforcement learning over noisy channels [J]. IEEE Journal on Selected Areas in Communications,2021,39（8）:2590-2603.

ZHAO Z L, CHEN C S, SHI H T, et al. Towards robust multi-UAV collaboration:MARL with noise-resilient communication and attention mechanisms:arXiv:2503. 02913[R]. Ithaca, NY, US:Cornell University,2025:2503. 02913.

SMITH K, ZHANG Z, AHMAD H M S, et al. Robust and safe multi-agent reinforcement learning framework with communication for autonomous vehicles: arXiv: 2506. 00982 [R]. Ithaca, NY, US:Cornell University,2025:2506. 00982.

WANG C Y, WANG Z W, AOUF N. Robust multi-agent reinforcement learning against adversarial attacks for cooperative self-driving vehicles[J]. IET Radar, Sonar & Navigation,2025, 19（1）:e70033.

XUE W Q, QIU W, AN B, et al. Mis-spoke or mis-lead:achieving robustness in multi-agent communicative reinforcement learning [C]∥Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems. Auckland, New Zealand: International Foundation for Autonomous Agents and Multiagent Systems,2022:1418-1426.

YU L B, QIU Y B, YAO Q M, et al. Robust communicative multi-agent reinforcement learning with active defense [C]∥WOOLDRIDGE M J, DY J G, NATARAJAN S. Thirty-eighth AAAI Conference on Artificial Intelligence, Thirty-sixth Conference on Innovative Applications of Artificial Intelligence, Fourteenth Symposium on Educational Advances in Artificial Intelligence. Vancouver, BC, Canada: AAAI Press, 2024:17575-17582.

RAZA S, SAPKOTA R, KARKEE M, et al. TRiSM for agentic AI:a review of trust, risk, and security management in LLM-based agentic multi-agent systems: arXiv: 2506. 04133 [R]. Ithaca, NY, US:Cornell University,2025:2506. 04133.

WU Z X, YE S C, HAN B, et al. Hijacking robot teams through adversarial communication [C]∥TAN J, TOUSSAINT M, DARVISH K. Proceedings of Conference on Robot Learning. Atlanta, GA, US:PMLR,2023,229:266-283.

MEDHI J K, LIU R, WANG Q L, et al. Robust multiagent reinforcement learning for UAV systems: countering byzantine attacks[J]. Information,2023,14（11）:623.

王璐，杨功流，蔡庆中，等.多智能体协同视觉SLAM技术研究进展[J].导航定位与授时，2020,7（3）:84-92.

WANG L, YANG G L, CAI Q Z, et al.Research progressin collaborative visual SLAM for multiple agent [J].Navigation Positioning & Timing,2020,7（3）:84-92.（in Chinese）

LE H C, SAEEDVAND S, HSU C C. A comprehensive review of mobile robot navigation using deep reinforcement learning algorithms in crowded environments [J]. Journal of Intelligent Robotic Systems,2024,110:158.

NARAYANASWAMY N G, KANEHIRO F. Vision-based software system for indoor quadrupedal locomotion: Integrated with SLAM, foothold planning, and multimodal gait [C]∥Proceedings of the 2024 IEEE/SICE International Symposium on System Integration. Ha Long, Vietnam:IEEE,2024:1330-1335.

CHAPPELLET K, MUROOKA M, CARON G, et al. Humanoid loco-manipulations using combined fast dense 3D tracking and SLAM with wide-angle depth-images[J]. IEEE Transactions on Automation Science and Engineering,2024,21（3）:3691-3704.

LÜ P, LI J, LAI J Z, et al. DRCM-CSLAM: distributed robust and communication-efficient multirobot cooperative LiDARinertial SLAM[J]. IEEE Transactions on Instrumentation and Measurement,2025,74:1-13.

YUGAY V, GEVERS T, OSWALD M R. MAGiC-SLAM:multi-agent gaussian globally consistent SLAM[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, TN, US: Computer Vision Foundation/IEEE,2025:6741-6750.

BIRD J, BLUMENKAMP J, PROROK A. DVM-SLAM:decentralized visual monocular simultaneous localization and mapping for multi-agent systems: arXiv: 2503. 04126 [R]. Ithaca, NY, US:Cornell University,2025:2503. 04126.

罗俊仁，张万鹏，袁唯淋，等.面向多智能体博弈对抗的对手建模框架[J].系统仿真学报，2022,34（9）:1941-1955.

LUO J R, ZHANG W P, YUAN W L, et al.Research on opponent modeling framework for multi-agent game confrontation [J].Journal of System Simulation,2022,34（9）:1941-1955.（in Chinese）

DU X Q, CHEN H C, WANG C, et al. Robust multi-agent reinforcement learning via Bayesian distributional value estimation[J]. Pattern Recognition,2024,145:109917.

LI Y Y, WANG Y J, ZHOU Y W. Multiagent deep reinforcement learning algorithms in StarCraft II:a review[J]. IEEE Access, 2024,12:167452-167470.

FU H, YOU M Y, ZHOU H J, et al. Closely cooperative multi-agent reinforcement learning based on intention sharing and credit assignment[J]. IEEE Robotics and Automation Letters, 2024,9（12）:11770-11777.

LI H, MAHJOUB H N, CHALAKI B, et al. Language grounded multi-agent reinforcement learning with human-interpretable communication[J]. Advances in Neural Information Processing Systems,2024,37:87908-87933.

HEUTHE V L, PANIZON E, GU H R, et al. Counterfactual rewards promote collective transport using individually controlled swarm microrobots [J]. Science Robotics, 2024, 9（97）:eado5888.

ZHOU Y, XIAO J H, ZHOU Y, et al. Multi-robot collaborative perception with graph neural networks[J]. IEEE Robotics and Automation Letters,2022,7（2）:2289-2296.

DE WIT J, VOGT P, KRAHMER E. The design and observed effects of robot-performed manual gestures:a systematic review [J]. ACM Transactions on Human-Robot Interaction, 2023, 12（1）.DOI:10.1145/3549530.

WANG C Y, ZHENG S Q, ZHONG L X, et al. PepperPose:fullbody pose estimation with a companion robot[C]∥Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. New York, NY, US: Association for Computing Machinery,2024.

REARDON C, LEE K, FINK J. Come see this! augmented reality to enable human-robot cooperative search [C]∥Proceedings of the 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics. Philadelphia, PA, US:IEEE,2018:1-7.

CHEN J Q, SUN B Y, POLLEFYES M, et al. A 3D mixed reality interface for human-robot teaming[C]∥Proceedings of the 2024 IEEE International Conference on Robotics and Automation. London, UK:IEEE,2023:11327-11333.

TABREZ A, LUEBBERS M B, HAYES B. Descriptive and prescriptive visual guidance to improve shared situational awareness in human-robot teaming[C]∥Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems. Richland, SC, US: International Foundation for Autonomous Agents and Multiagent Systems,2022:1256-1264.

KADUK J, CAVDAN M, DREWING K, et al. Effects of humanswarm interaction on subjective time perception:Swarm size and speed[C]∥Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction. Washington, D. C. , US:IEEE,2023.

DAHIYA A, AROYO A M, DAUTENHAHN K, et al. A survey of multi-agent human-robot interaction systems [J]. Robotics and Autonomous Systems,2023,161:104335.

RETZLAFF C O, DAS S, WAYLLACE C, et al. Human-in-the-loop reinforcement learning: a survey and position on requirements, challenges, and opportunities [J]. Journal of Artificial Intelligence Research,2024,79:359-415.

杨强，童咏昕，王晏晟，等.群体智能中的联邦学习算法综述[J].智能科学与技术学报，2022,4（1）:27-44.

YANG Q, TONG Y X, WANG Y S, et al.A survey on federated learning in crowd intelligence[J].Chinese Journal of Intelligent Science and Technology,2022,4（1）:27-44.（in Chinese）

RUAN L H, MONDAL S, DIAS I, et al. Low-latency federated reinforcement learning-based resource allocation in converged access networks[C]∥Proceedings of the 2020 Optical Fiber Communications Conference and Exhibition. San Diego, CA, US:IEEE,2020:1-3.

XU M R, PENG J L, GUPTA B B, et al. Multiagent federated reinforcement learning for secure incentive mechanism in intelligent cyber-physical systems[J]. IEEE Internet of Things Journal,2022,9（22）:22095-22108.

WANG H, HE S H, ZHANG Z L, et al. Momentum for the win:collaborative federated reinforcement learning across heterogeneous environments[C]∥Proceedings of the 41st International Conference on Machine Learning. Vienna, Austria: JMLR. org, 2024.

NA S, ROUČEK T, ULRICH J, et al. Federated reinforcement learning for collective navigation of robotic swarms[J]. IEEE Transactions on Cognitive and Developmental Systems, 2023, 15（4）:2122-2131.

SIERRA-GARCÍA J E, SANTPS M. Federated discrete reinforcement learning for automatic guided vehicle control[J].Generation Computer Systems,2024,150:78-89.

XUAN M X, YU L K, SUN X, et al. Clustered federated reinforcement learning for autonomous UAV control in air corridors [J]. IEEE Open Journal of Vehicular Technology, 2025,6:1582-1592.

PINTO NETO E C, SADEGHI S, ZHANG X, et al. Federated reinforcement learning in IoT: applications, opportunities and open challenges[J].Applied Sciences,2023,13（11）:6497.

LIANG Y Y, LI B W. Parallel knowledge transfer in multi-agent reinforcement learning:arXiv:2003. 13085v1[R]. Ithaca, NY, US:Cornell University,2020:2003. 13085v1.

MAI Y X, ZANG Y F, YIN Q Y, et al. Deep multitask multiagent reinforcement learning with knowledge transfer [J]. IEEE Transactions on Games,2024,16（3）:566-576.

BADIHI G, GRAHAM K E, GRUND C, et al. Chimpanzee gestural exchanges share temporal structure with human language [J]. Current Biology,2024,34（14）:R673-R674.

PENG X, WANG L M, SHAO C C, et al. Avian acoustic communication: understanding of peripheral and central neural systems with ecological adaptations[J]. Avian Research,2025, 16（2）:100248.

MIER QUESADA Z, PORTILLO W, PAREDES R G. Behavioral evidence of the functional interaction between the main and accessory olfactory system suggests a large olfactory system with a high plastic capability[J]. Frontiers in Neuroanatomy,2023, 17:1211644.

PEDRAJA F, SAWTELL N B. Collective sensing in electric fish [J]. Nature,2024,628（8006）:139-144.

NAKAGAWA H, KANEZAKI A. Multi-agent visual coordination using optical wireless communication [J]. IEEE Robotics and Automation Letters,2023,8（11）:7857-7864.

WANG H, CHEN B B, ZHANG T Y, et al. Learning to communicate through implicit communication channels: arXiv:2411. 01553 [R]. Ithaca, NY, US: Cornell University, 2024:2411. 01553.

LI D P, LOU N, XU Z W, et al. Efficient communication in multi-agent reinforcement learning with implicit consensus generation[C]∥Proceedings of the Sponsored by the Association for the Advancement of Artificial Intelligence. Philadelphia, PA, US:AAAI Press,2025:23240-23248.

HAO S B, GU Y, MA H D, et al. Reasoning with language model is planning with world model:arXiv:2305. 14992 [R]. Ithaca, NY, US:Cornell University,2023:2305. 14992.

ZHANG C Y, YANG K J, HU S Y, et al. ProAgent:building proactive cooperative agents with large language models[C]∥Proceedings of the Thirty-eighth AAAI Conference on Artificial Intelligence, Thirty-sixth Conference on Innovative Applications of Artificial Intelligence, Fourteenth Symposium on Educational Advances in Artificial Intelligence. Vancouver, BC, Canada:AAAI Press,2024:17591-17599.

GONG R, HUANG Q Y, MA X J, et al. MindAgent:emergent gaming interaction [C]∥Proceedings of the Findings of the Association for Computational Linguistics. Mexico City, Mexico:Association for Computational Linguistics,2024:3154-3183.

PETERS J, DE PUISEAU C W, TERCAN H, et al. Emergent language:a survey and taxonomy [J]. Autonomous Agents and Multi-Agent Systems,2025,39（1）:18.

YU W H, ZHAO J. Quantum multi-agent reinforcement learning as an emerging AI technology: a survey and future directions [C]∥Proceedings of the 2023 International Conference on Computer and Applications. Cairo, Egypt:IEEE,2023:1-7.

CHEN W Z, WAN J W, YE F W, et al. QMARL: a quantum multi-agent reinforcement learning framework for swarm robots navigation [C]∥Proceedings of the 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops. Seoul, Korea:IEEE,2024:388-392.

DERIEUX A, SAAD W. eQMARL: entangled quantum multi-agent reinforcement learning for distributed cooperation over quantum channels: arXiv: 2405. 17486 [R]. Ithaca, NY, US:Cornell University,2024:2405. 17486.

WANG M Y, LI J H, MA M Y, et al. SNN-SC: a spiking semantic communication framework for collaborative intelligence [J]. IEEE Transactions on Vehicular Technology,2024,74（4）:5883-5896.

PARK S H, KIM G S, HAN Z, et al. Quantum multi-agent reinforcement learning is all you need:coordinated global access in integrated tn/ntn cube-satellite networks [J]. IEEE Communications Magazine,2024,62（10）:86-92.

YUN W J, KIM J P, JUNG S Y, et al. Quantum multiagent actorcritic neural networks for Internet-connected multirobot coordination in smart factory management[J]. IEEE Internet of Things Journal,2023,10（11）:9942-9952.

Views

下载量

CNKI被引量

Alert me when the article has been cited

提交

Tools

Publicity Resources

Regional Air Defense and Anti-missile Weapon-Target Assignment Based on Multi-agent Reinforcement Learning

Dynamic Decision-making Method of Unmanned Platform Chaff Jamming for Terminal Defense Based on Multi-agent Deep Reinforcement Learning

Collaborative Regional Information Collection Strategy Based on MLAT-DRL Algorithm

Multi-agent Reinforcement Learning-based Offloading Decision for UAV Cluster Combat Tasks

Related Author

BO Yuming

FAN Boyang

ZHANG Baoheng

WANG Yuanhao

WU Xiang

YAN Yan

Tao DING

Silai WAN

Related Institution

School of Automation, Nanjing University of Science and Technology

Dalian Changfeng Industrial Corporation

Shanghai Aerospace System Engineering Institute

School of Mechanical Engineering, Beijing Institute of Technology

Yangtze Delta Region Academy of Beijing Institute of Technology(Jiaxing)

Postal code：100089
Tel：010-68963060/68962718 Fax：010-68963025 Email：bgxb@cos.org.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备05059581号-4 京公网安备11010802024360号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：119049 Visits Today：36

⁰