Cooperative MARL

MARL: Multi-Agent Reinforcement Learning

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22 papers in the last four weeks, up 450% on the four weeks before. 0.2% of all new papers.

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Coordination Matters: Evaluation of Cooperative Multi-Agent Reinforcement Learning
May 7, 2026Maria Ana Cardei, Matthew Landers, Afsaneh DoryabMulti-Agent Coordination AI Agent Benchmarks
University of Virginia
Modular Reinforcement Learning For Cooperative Swarms
May 6, 2026Erel Shtossel, Gal A. KaminkaMulti-Robot Systems Reinforcement Learning
Bar Ilan University Ramat Gan, Israel
Structural Equivalence and Learning Dynamics in Delayed MARL
May 5, 2026Jules Sintes, Ana Bušić, Jiamin ZhuPartially Observable Markov Decision Processes Cooperative MARL
Inria · DIENS, École Normale Supérieure, PSL University · Paris, France +2
Quality-Aware Exploration Budget Allocation for Cooperative Multi-Agent Reinforcement Learning
May 3, 2026Dahyun Oh, Minhyuk Yoon, H. Jin KimMulti-Agent Coordination Intrinsic Reward Methods
Department of Aerospace Engineering, Seoul National University, Seoul, Republic of Korea
MAGIC: Multi-Step Advantage-Gated Causal Influence for Multi-agent Reinforcement Learning
May 3, 2026Haohan Yu, Jinmiao Cong, Shengzhi Wang +2Multi-Agent Coordination Intrinsic Reward Methods
1Dalian University of Technology · 2Microsoft, Beijing, China
NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search
May 1, 2026Sizhe Tang, Zuyuan Zhang, Mahdi Imani +1Multi-Agent System Optimization Multi-Agent Coordination
The George Washington University · Northeastern University
PALCAS: A Priority-Aware Intelligent Lane Change Advisory System for Autonomous Vehicles using Federated Reinforcement Learning
Apr 29, 2026Yassine Ibork, Nhat Ha Nguyen, Myounggyu Won +1Federated RL Autonomous Driving
School of Computing, Wichita State University · Department of Computer Science, University of Memphis
Quantum Frog: Emergent Cooperation and Difficulty Scaling in a Quantized-Time Cooperative Game
Apr 22, 2026Saad MankariousMulti-Agent Coordination Game-Playing Agents
M $^{2}$ GRPO: Mamba-based Multi-Agent Group Relative Policy Optimization for Biomimetic Underwater Robots Pursuit
Apr 21, 2026Yukai Feng, Zhiheng Wu, Zhengxing Wu +2Multi-Robot Systems Group Relative Policy Optimization
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China, and also with the Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China
SynAgent: Generalizable Cooperative Humanoid Manipulation via Solo-to-Cooperative Agent Synergy
Apr 20, 2026Wei Yao, Haohan Ma, Hongwen Zhang +6Robotic Control HOI Generation
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China · School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China +2
Scalable Neighborhood-Based Multi-Agent Actor-Critic
Apr 20, 2026Tim Goppelsroeder, Rasmus JensenMulti-Agent Systems Actor-Critic Methods
Department of Computer Science, ETH Zürich.
ConventionPlay: Capability-Limited Training for Robust Ad-Hoc Collaboration
Apr 20, 2026Abhishek Sriraman, Eleni Vasilaki, Robert LoftinMulti-Agent Coordination Multi-Agent Collaboration
University of Sheffield · Sheffield, United Kingdom
COSAC: Counterfactual Credit Assignment in Sequential Cooperative Teams
Apr 20, 2026Shripad Deshmukh, Jayakumar Subramanian, Raghavendra Addanki +1Sequential MARL Credit Assignment in RL
College of Information and Computer Sciences University of Massachusetts Amherst · Adobe Research San Jose, CA
Do LLM-derived graph priors improve multi-agent coordination?
Apr 19, 2026Nikunj Gupta, Rajgopal Kannan, Viktor PrasannaMulti-Agent Coordination LLM-Guided RL
University of Southern California, Los Angeles, California, United States · DEVCOM ARL Army Research Oﬀice, Los Angeles, California, United States
The Price of Paranoia: Robust Risk-Sensitive Cooperation in Non-Stationary Multi-Agent Reinforcement Learning
Apr 17, 2026Deep Kumar Ganguly, Chandradithya S Jonnalagadda, Pratham Chintamani +1Cooperative Game Theory Multi-Agent Coordination
Technical University of Munich (TU Munich) · Brown University · Indian Institute of Technology Tirupati (IIT Tirupati)
SAT: Sequential Agent Tuning for Coordinator Free Plug and Play Multi-LLM Training with Monotonic Improvement Guarantees
Apr 17, 2026Yi Xie, Yangyang Xu, Yi Fan +1LLM Agent Training Decentralized MARL
Department of Electrical & Computer Engineering, University of Arizona Tucson, Arizona, USA · Department of Mathematical Sciences, Rensselaer Polytechnic Institute Troy, New York, USA · Amazon Web Services New York, USA
Communication Gain and Delay Cost Under Cross-Timestep Delays in Cooperative Multi-Agent Reinforcement Learning
Apr 4, 2026Zihong Gao, Hongjian Liang, Lei Hao +1Partially Observable Markov Decision Processes Multi-Agent Communication
The State Key Laboratory for Manufacturing Systems Engineering School of Automation Science and Engineering, Xi’an Jiaotong University
Marginal-Contribution Policy Gradients under Filtered Feedback for Multi-Agent LLMs
Apr 3, 2026Elai Ben-Gal, Stela TongMulti-Agent LLM Systems Multi-Agent System Optimization
Department of Mathematics Stanford University · Graduate School of Business Stanford University
Multi-Agent Reinforcement Learning Counteracts Delayed CSI in Multi-Satellite Systems
Mar 17, 2026Marios Aristodemou, Yasaman Omid, Sangarapillai Lambotharan +2Wireless Communications Proximal Policy Optimization
Department of Computer Science, University of York, York, U.K. · Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, U.K. · Institute for Digital Technologies, Loughborough University (London Campus), London, U.K. +1
Explicit Credit Assignment through Local Rewards and Dependence Graphs in Multi-Agent Reinforcement Learning
Jan 29, 2026Bang Giang Le, Viet Cuong TaCredit Assignment in RL Credit Assignment
Human-Machine Interaction Laboratory VNU University of Engineering and Technology, Hanoi, Vietnam
A Game-Theoretic Spatio-Temporal Reinforcement Learning Framework for Collaborative Public Resource Allocation
Oct 30, 2025Songxin Lei, Qiongyan Wang, Yanchen Zhu +6Multi-Agent Coordination Reinforcement Learning
The Hong Kong University of Science and Technology (Guangzhou) · Peking University · Beijing Institute of Technology +1
Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration
Sep 12, 2025Chirayu Nimonkar, Shlok Shah, Catherine Ji +1Multi-Agent Coordination RL Exploration
Princeton University
Unsupervised Partner Design Enables Robust Ad-hoc Teamwork
Aug 8, 2025Constantin Ruhdorfer, Matteo Bortoletto, Victor Oei +2Multi-Agent Collaboration Ad Hoc Teamwork
Collaborative Artificial Intelligence, University of Stuttgart, Stuttgart, Germany.
ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork
May 29, 2025Caroline Wang, Arrasy Rahman, Benjamin Nativi +4Multi-Agent Collaboration Ad Hoc Teamwork
Department of Computer Science The University of Texas at Austin · Visa AI · College of Computing and Data Science Nanyang Technological University +1
CoSkill: Joint Reinforcement Learning of Reasoning and Meta-Skill Agents for Hierarchical Skill Evolution
Date pendingJinyuan Feng, Dongmin Li, Yiqun Chen +4Hierarchical RL LLM Agent Skill Learning
Institute of Automation, Chinese Academy of Sciences · Renmin University of China · ByteDance
Certifying cooperation: a novel approach to cooperative multi-agent task generation
Date pendingYannick Molinghen, Hugo Charels, Tom LenaertsMulti-Agent Systems Cooperative MARL
Machine Learning Group, Universit´e Libre de Bruxelles, Belgium. · AI Lab, Vrije Universiteit Brussel, Belgium. · Center for Human-Compatible AI, UC Berkeley, USA.

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Coordination Matters: Evaluation of Cooperative Multi-Agent Reinforcement Learning

Maria Ana Cardei, Matthew Landers, Afsaneh Doryab

University of Virginia

Multi-Agent Coordination AI Agent Benchmarks Multi-Robot Task Allocation Cooperative MARL

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Cooperative multi-agent reinforcement learning (MARL) benchmarks commonly emphasize aggregate outcomes such as return, success rate, or completion time. While essential, these metrics often fail to reveal how agents coordinate, particularly in settings where agents, tasks, and joint assignment choices scale combinatorially. We propose a coordination-aware evaluation perspective that supplements return with process-level diagnostics. We instantiate this perspective using STAT, a controlled commitment-constrained spatial task-allocation testbed that systematically varies agents, tasks, and environment size while holding observation access and task rules fixed. We evaluate six representative value-based MARL methods across varying levels of centralization. Our results show that similar return trends can reflect distinct coordination mechanisms, including differences in redundant assignment, assignment diversity, and task-completion efficiency. We find that in commitment-constrained task allocation, performance under scale is shaped not only by nominal action-space size, but also by assignment pressure, sparse decision opportunities, and redundant choices among interdependent agents. Our findings motivate coordination-aware evaluation as a necessary complement to return-based benchmarking for cooperative MARL.

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