Cooperative MARL

MARL: Multi-Agent Reinforcement Learning

Ask about this topic

Overview

Cooperative MARL studies how multiple learning agents can achieve shared goals when each sees only part of the environment and may act independently. Common approaches include centralized training with decentralized execution, value factorization such as QMIX, actor-critic methods such as MAPPO, and graph-based communication or hierarchical control. Researchers evaluate coordination on benchmarks such as StarCraft Multi-Agent Challenge, Multi-Particle Environment, and Overcooked, as well as in robotics, traffic control, and resource allocation. A central challenge is making coordination scalable and robust to unfamiliar teammates, changing team sizes, communication failures, and tasks where high team reward may not reflect genuine cooperation.

Latest papers

CardsList

247 papers32 papers in the last four weeks, up 10% on the four weeks before. 0.2% of all new papers.

  1. Agentic-Kube: A Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-Objective Kubernetes Scheduling

    Mar 12, 2026Hamed HamzehMulti-Objective Reinforcement LearningCooperative MARL

    University of Westminster Computer Science and Engineering London, United Kingdom

  2. The price of decentralization in managing engineering systems through multi-agent reinforcement learning

    Mar 12, 2026Prateek Bhustali, Pablo G. Morato, Konstantinos G. Papakonstantinou +1Decentralized MARLMulti-Agent Coordination

    Faculty of Architecture and the Built Environment, Delft University of Technology, Delft, 2628 BL, The Netherlands · Engineering Risk Analysis Group, Technical University of Munich, Munich, 80333, Germany · Department of Civil & Environmental Engineering, The Pennsylvania State University, University Park, PA, 16802, USA

  3. DeReCo: Decoupling Representation and Coordination Learning for Object-Adaptive Decentralized Multi-Robot Cooperative Grasping and Lifting

    Mar 9, 2026Kazuki Shibata, Ryosuke Sota, Shandil Dhiresh Bosch +3Multi-Robot Cargo TransportationMulti-Robot Coordination

    All the authors are with the Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), Nara, Japan. · Shandil Dhiresh Bosch is with the Department of Cognitive Robotics, Faculty of Mechanical Engineering, Delft University of Technology, Delft, Netherlands.

  4. TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Size

    Mar 9, 2026Stefan Lionar, Gim Hee LeeHuman-Object InteractionHOI Generation

    Garena · Sea AI Lab · National University of Singapore

  5. Learning to Reflect: Hierarchical Multi-Agent Reinforcement Learning for CSI-Free mmWave Beam-Focusing

    Mar 7, 2026Hieu Le, Oguz Bedir, Mostafa Ibrahim +2Millimeter-Wave CommunicationsWireless Communications

    Texas A&M University

  6. Multi-Agent Reinforcement Learning with Submodular Reward

    Mar 6, 2026Wenjing Chen, Chengyuan Qian, Shuo Xing +2Cooperative MARLMulti-Agent Reinforcement Learning

    Department of Computer Science and Engineering, Texas A&M University

  7. SCoUT: Scalable Communication via Utility-Guided Temporal Grouping in Multi-Agent Reinforcement Learning

    Mar 5, 2026Manav Vora, Gokul Puthumanaillam, Hiroyasu Tsukamoto +1Multi-Agent CommunicationMulti-Agent Reinforcement Learning

    University of Illinois Urbana-Champaign

  8. Dual-Interaction-Aware Cooperative Control Strategy for Alleviating Mixed Traffic Congestion

    Mar 4, 2026Zhengxuan Liu, Yuxin Cai, Yijing Wang +3Intelligent Transportation SystemsMulti-Agent Control

    Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China · School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore · Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China

  9. Cognition to Control - Multi-Agent Learning for Human-Humanoid Collaborative Transport

    Mar 4, 2026Hao Zhang, Yisen Li, Ruize Geng +4Human-Robot CollaborationMulti-Agent Collaboration

    University of Texas at Arlington. · Carnegie Mellon University

  10. Learning Approximate Nash Equilibria in Cooperative Multi-Agent Reinforcement Learning via Mean-Field Subsampling

    Mar 4, 2026Emile Anand, Ishani KarmarkarMean-Field MARLReinforcement Learning

    Georgia Institute of Technology, Atlanta, GA · Stanford University, Palo Alto, CA

  11. Hybrid Belief Reinforcement Learning for Efficient Coordinated Spatial Exploration

    Mar 4, 2026Danish Rizvi, David BoyleUAV CommunicationReinforcement Learning

    Imperial College London, UK

  12. Heterogeneous Agent Collaborative Reinforcement Learning

    Mar 3, 2026Zhixia Zhang, Zixuan Huang, Gongxun Li +10Multi-Agent CollaborationHeterogeneous MARL

    Beihang University · Bytedance China · Apple +2

  13. Personalized Multi-Agent Average Reward TD-Learning via Joint Linear Approximation

    Mar 2, 2026Leo Muxing Wang, Pengkun Yang, Lili SuTemporal-Difference LearningMulti-Agent Reinforcement Learning

    Northeastern University · Tsinghua University

  14. MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning

    Feb 28, 2026Tianmeng Hu, Biao Luo, Chunhua Yang +1Multi-Agent Reinforcement LearningMulti-Objective Reinforcement Learning

    School of Automation, Central South University, Changsha, 410012, China. · Texas A&M University at Qatar, Doha, 23874, Qatar.

  15. Sharing is caring: data sharing in multi-agent supply chains

    Feb 27, 2026Wan Wang, Haiyan Wang, Adam SobeyMulti-Agent SystemsSupply Chain Management

    School of Transportation and Logistics Engineering, Wuhan University of Technology, No.1178 Heping Avenue Wuchang District, Wuhan, 430063, Hubei, China · Maritime Engineering, University of Southampton, Burgess Rd, Southampton, SO17 1BJ, UK · Sustainability Mission, The Alan Turing Institute, The British Library, London, NW1 2DB, UK

  16. QSIM: Mitigating Overestimation in Multi-Agent Reinforcement Learning via Action Similarity Weighted Q-Learning

    Feb 26, 2026Yuanjun Li, Bin Zhang, Hao Chen +3Reinforcement LearningCooperative MARL

    Shandong University · The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences · Li Auto Inc. +1

  17. Hierarchical Lead Critic based Multi-Agent Reinforcement Learning

    Feb 25, 2026David Eckel, Henri MeeßMulti-Agent Reinforcement LearningCooperative MARL

    Fraunhofer Institute IVI, Ingolstadt, Germany

  18. Training Generalizable Collaborative Agents via Strategic Risk Aversion

    Feb 25, 2026Chengrui Qu, Yizhou Zhang, Nicolas Lanzetti +1Zero-Shot CoordinationCooperative MARL

    Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.

  19. Cooperative-Competitive Team Play of Real-World Craft Robots

    Feb 24, 2026Rui Zhao, Xihui Li, Yizheng Zhang +6Cooperative MARLMulti-Robot Coordination

    The authors are with Tencent Robotics X Laboratory, Shenzhen, Guangdong, China. · The author is with Tsinghua University, Shenzhen, Guangdong, China.

  20. Probing Dec-POMDP Reasoning in Cooperative MARL

    Feb 24, 2026Kale-ab Tessera, Leonard Hinckeldey, Riccardo Zamboni +2Cooperative MARLMulti-Agent Reasoning

    University of Edinburgh, Edinburgh, United Kingdom · Politecnico di Milano, Milan, Italy

  21. Descent-Guided Policy Gradient for Scalable Cooperative Multi-Agent Learning

    Feb 23, 2026Shan Yang, Yang LiuCooperative MARLMulti-Agent Reinforcement Learning

    National University of Singapore

  22. Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learning

    Feb 19, 2026Yonghyeon Jo, Sunwoo Lee, Seungyul HanCooperative MARLMulti-Agent Reinforcement Learning

    Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea 44919

  23. NestRL: A Nested Training Regime for Mutual Adaptation in Human-AI Teaming

    Feb 18, 2026Upasana Biswas, Durgesh Kalwar, Subbarao Kambhampati +1Human-AI CollaborationMulti-Agent Coordination

    School of Computing and AI, Arizona State University, Tempe, AZ, USA · Department of Computer Science, Colorado State University, Fort Collins, CO, USA

  24. Dual-Quadruped Collaborative Transportation in Narrow Environments via Safe Reinforcement Learning

    Feb 18, 2026Zhezhi Lei, Zhihai Bi, Wenxin Wang +1Multi-Robot Cargo TransportationMulti-Robot Systems

    Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China · Advanced Remanufacturing & Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR), Singapore 637143 · Cheng Kar-Shun Robotics Institute, The Hong Kong University of Science and Technology, Hong Kong SAR, China

  25. Multi-agent cooperation through in-context co-player inference

    Feb 18, 2026Marissa A. Weis, Maciej Wołczyk, Rajai Nasser +4Cooperative MARLDecentralized MARL

    Google, Paradigms of Intelligence Team · Santa Fe Institute

  26. Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learning

    Feb 18, 2026Emile Anand, Richard Hoffmann, Sarah Liaw +1Reinforcement LearningCooperative MARL

    School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA · Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA · Kempner Institute, Harvard University, Cambridge, MA, USA

  27. Fairness over Equality: Correcting Social Incentives in Asymmetric Sequential Social Dilemmas

    Feb 17, 2026Alper Demir, Hüseyin Aydın, Kale-ab Abebe Tessera +2Cooperative MARLMulti-Agent Reinforcement Learning

    Izmir University of Economics & University of Edinburgh, Izmir, Turkey · Middle East Technical University & Utrecht University, Ankara, Turkey · University of Edinburgh, Edinburgh, United Kingdom +1

  28. Explicit Credit Assignment through Local Rewards and Dependence Graphs in Multi-Agent Reinforcement Learning

    Jan 29, 2026Bang Giang Le, Viet Cuong TaCredit Assignment in RLCredit Assignment

    Human-Machine Interaction Laboratory VNU University of Engineering and Technology, Hanoi, Vietnam

  29. Does Centralized Learning Help Embodied Agents Coordinate, and When Can a Tabular Study Tell?

    Jan 24, 2026Muhammad Ahmed Atif, Nehal Naeem Haji, Muhammad Ebad Atif +1Multi-Agent Reinforcement LearningCooperative MARL

    Dhanani School of Science and Technology, Habib University, Karachi, Pakistan

  30. Distributed scalable coupled policy algorithm for networked multi-agent reinforcement learning

    Dec 5, 2025Pengcheng Dai, Dongming Wang, Wenwu Yu +1Cooperative MARLDecentralized MARL

    Engineering Systems and Design Pillar, Singapore University of Technology and Design, Singapore 487372 · Department of Electrical and Computer Engineering, University of California, Riverside, CA 92521, USA · Frontiers Science Center for Mobile Information Communication and Security, School of Mathematics, Southeast University, Nanjing 210096, China, and also with Purple Mountain Laboratories, Nanjing 211102, China