Cooperative MARL

MARL: Multi-Agent Reinforcement Learning

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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. Cooperative EV Charging Assignment Using Multi-Agent Reinforcement Learning: A Comparison of QMIX and Independent Q-Learning Under Urban Traffic Uncertainty

    Oct 27, 2026Nour-Eddine Moumni, Rachid Alaoui, Driss KiouachElectric Vehicle ChargingCooperative MARL

    Laboratory of Research in Computer Science and Telecommunications (LRIT), Mohammed V University, Rabat, Morocco · Higher School of Technology of Salé (EST Salé), Mohammed V University, Salé, Morocco · L2MASI Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco

  2. GARDEN: Generalist Autonomous Reward Design Through Evidence Navigation for Cooperative Multi-Agent Reinforcement Learning

    Oct 27, 2026Dogan UrgunReward Design for RLCooperative MARL

    Department of Electrical and Electronics Engineering, Karabuk University, 78050 Karabuk, Türkiye

  3. TANDEM: Balancing Centralized and Decentralized Critics to Address the Information Gap in Complex Multi-Agent Environments

    Oct 27, 2026Alireza Saleh Abadi, Leen-Kiat Soh, Adam Eck +1Cooperative MARLCentralized Training with Decentralized Execution

    School of Computing, University of Nebraska-Lincoln, Lincoln, Nebraska, USA · Department of Computer Science, Oberlin College, Oberlin, Ohio, USA · Institute for AI & School of Computing, University of Georgia, Athens, Georgia, USA

  4. TeamGym: A Gym for Team Decision-Making by AI Agents with Distributed Information

    Oct 27, 2026Chanwoo Park, Jaywoong Jeong, Zachary Wojtowicz +4Multi-Agent CollaborationMulti-Agent Systems

    MIT · KAIST · UT Austin +1

  5. Preference-Guided Cooperation in Multi-Objective Multi-Agent Reinforcement Learning

    Oct 26, 2026Qiyue Xia, J. Michael HerrmannMulti-Objective Reinforcement LearningMulti-Agent Reinforcement Learning

    School of Informatics, University of Edinburgh, Edinburgh, United Kingdom

  6. Belief-Conditioned Multi-Agent Actor Critic for Decentralized LLM Collaboration

    Oct 26, 2026Tianle Chen, Shuo Liu, Christopher AmatoMulti-Agent LLM SystemsMulti-Agent Reinforcement Learning

    Khoury College of Computer Sciences, Northeastern University, Boston, MA, 02215, USA

  7. Centralized Critics for N-Agent Ad Hoc Teamwork

    Oct 26, 2026Andrew McAlinden, Caroline Wang, Steven Shi +3Ad Hoc TeamworkMulti-Agent Coordination

  8. IntNet: A Communication-Driven Multi-Agent Reinforcement Learning Framework for Cooperative Autonomous Driving

    Oct 25, 2026Leandro Parada, Kevin Yu, Panagiotis AngeloudisRL for Autonomous DrivingCooperative MARL

    L. Parada, K. Yu, and P. Angeloudis are with the Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London, UK

  9. HA-DiPO: Diffusion Policy Optimization for Heterogeneous Cooperative Transport

    Oct 24, 2026Xuebin Li, Jinpeng Xiao, Zong Chen +4Multi-Robot Cargo TransportationCooperative MARL

    School of Mathematics, Harbin Institute of Technology, Harbin, China · State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China · Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy

  10. Cognitively Constrained Partners for Zero-Shot Coordination in Real-Time Partitioned Control

    Oct 22, 2026Ash Simmons, George De Ath, Richard M. EversonZero-Shot CoordinationCooperative MARL

    University of Exeter, UK

  11. Cooperative Multi-Agent Reinforcement Learning for Market Making on Prediction-Market Order Books

    Oct 19, 2026Skander LejmiAlgorithmic TradingCooperative MARL

    Department of Statistics, London School of Economics and Political Science

  12. Learning Who Talks to Whom: Role-Aware Communication Topology Scheduling for Efficient LLM Multi-Agent Collaboration

    Oct 19, 2026Haibin Li, Wenya Yu, Xuefeng Jiang +4Multi-Agent Communication TopologyMulti-Agent LLMs

    Beihang University · Nanyang Technological University · University of Chinese Academy of Sciences

  13. Cooperation Without Shared Rewards via Reciprocal Q-Augmentation

    Oct 17, 2026Francesco Morri, Adel Abusitta, Quentin Cappart +1Multi-Agent Reinforcement LearningCooperative MARL

    Polytechnique Montréal Montréal, Canada · Polytechnique Montréal, Mila Montréal, Canada · UCLouvain Louvain-la-Neuve, Belgium +1

  14. Gossip Agents: Reputation through Opinion Dynamics over Evolving Networks

    Oct 15, 2026Yutong Wu, Arlei SilvaCooperative MARLMulti-Agent Communication

    Rice University Houston, United States

  15. Learning Self-Orchestration in Decentralized Agent Swarms

    Oct 15, 2026Guowei Xu, Zhenting Qi, Amani Kiruga +2Multi-Agent OrchestrationMulti-Agent Coordination

    Harvard University

  16. Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning

    Oct 13, 2026Svar Rajankumar Patel, Kshitij JerathBehavioral Diversity in RLCooperative MARL

    Department of Computer Science, University of Massachusetts Lowell, Lowell, Massachusetts, USA · Department of Mechanical and Industrial Engineering, University of Massachusetts Lowell, Lowell, Massachusetts, USA

  17. Compositional RL: Evaluating and Training LLMs for Collaborative Reasoning

    Oct 12, 2026Shangbin Feng, Yulia Tsvetkov, Jan Kautz +1Multi-LLM CollaborationRL for Language Models

    University of Washington · NVIDIA

  18. Position: Beyond Training Time, Test-Time Coordination is Essential for Cooperative MARL

    Oct 12, 2026Dongsu Lee, Sooraj Sathish, Joonkyung Kim +2Centralized Training with Decentralized ExecutionCooperative MARL

    The University of Texas at Austin · Texas A&M University · KAIST

  19. PopGen: Generating Semantically Diverse Populations for Zero-Shot Coordination

    Oct 11, 2026Paul Teiletche, Kanghyun Ryu, Kyungjoon Park +3Zero-Shot CoordinationMulti-Agent Coordination

    EPFL · UC Berkeley · Yonsei University +2

  20. SIGN-MAPPO: State-Intent-Aware Graph Multi-Agent Reinforcement Learning for Cooperative Driving under Imperfect V2X

    Oct 11, 2026Zide Fan, Jiahui Yuan, Kaixin Zhang +3Multi-Agent CoordinationCooperative MARL

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China · Key Laboratory of Target Cognition and Application Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences and the School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences

  21. A Reinforcement Learning Framework for Safety-Aware Haptic Shared Control in Surgical Robotics

    Oct 11, 2026Zihao Zhang, Karl Handwerker, Balint VargaRobotic ControlHuman-Robot Collaboration

    Institute of Control Systems, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany

  22. ConventionPlay: Capability-Limited Training for Robust Ad-Hoc Collaboration

    Oct 8, 2026Abhishek Sriraman, Eleni Vasilaki, Robert LoftinMulti-Agent CollaborationAd Hoc Teamwork

    Department of Computer Science Sheffield, UK · Pittsburgh, PA 15213, USA

  23. Continual Graph Multi-Agent Reinforcement Learning

    Oct 7, 2026Tommaso Marzi, Ahmed Hendawy, Jan Peters +3Multi-Agent Reinforcement LearningContinual RL

    IDSIA USI-SUPSI, Università della Svizzera italiana, Lugano, Switzerland · Technical University of Darmstadt, Darmstadt, Germany · Robotics Institute Germany (RIG) +4

  24. Learning to Report Unsafe Tasks in a Multi-Agent Game

    Oct 6, 2026Avyay M. Casheekar, Hariganesh TangiralaAI Agent SafetyMulti-Agent Systems

    University of Michigan Law School Ann Arbor, USA · School of Computing National University of Singapore Singapore, Singapore

  25. Partially Observable Zero-shot coordination by Predicting Intention of Partner

    Oct 6, 2026Jinnyeong Yang, Yuhwan Jeong, Hoyong Kwon +3Multi-Agent CoordinationPartially Observable RL

    KAIST, Visual Intelligence Lab

  26. Self-Referenced Social Preferences: Cooperation without Observing Others Rewards

    Oct 6, 2026Mohamed Ayman Mohamed, Harshil Kotamreddy, Marcos Menon JoseCooperative MARLMulti-Agent Reinforcement Learning

    Amazon, Vancouver, Canada · Nvidia, Santa Clara, United States · Itaú Unibanco, São Paulo, Brazil

  27. Cooperating with Future Collaborators: Multi-Agent RL under Staggered Participation

    Oct 6, 2026Jianglin Qiao, Siyi Hu, Thien Hoang Nguyen +2Multi-Agent Reinforcement LearningCooperative MARL

    ACFR, The University of Sydney, Sydney, Australia · School of EECMS, Curtin University, Bentley, Australia · School of CSIT, Adelaide University, Adelaide, Australia

  28. Who Bears the Burden? Learning Responsibility for Shared Constraints in Multi-Agent Reinforcement Learning

    Oct 5, 2026Xiaoyang Cao, Jingqi Li, Zhe Fu +1Game TheoryConstrained MARL

    Massachusetts Institute of Technology · The University of Texas at Austin · Stanford University +1

  29. Grounded Joint-Attention Other-Play for Zero-Shot Coordination

    Oct 5, 2026Giulia Benintendi, Constantin Ruhdorfer, Fabian Kögel +1Multi-Agent CoordinationVisual Attention

    University of Zurich · University of Stuttgart

  30. Synergizing Drone Delivery Order Pooling and Road Network Monitoring through Monitoring-Task Orderization

    Oct 4, 2026Yulong Hu, Meng Xu, Sen Li +1Aerial RoboticsIntelligent Transportation Systems

    Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China · School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland