Cooperative MARL

MARL: Multi-Agent Reinforcement Learning

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

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

Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
May 18, 2026Sunwoo Lee, Mingu Kang, Yonghyeon Jo +1Adversarial Training Multi-Agent Coordination
Graduate School of Artificial Intelligence, UNIST, Ulsan, South Korea
Shared Backbone PPO for Multi-UAV Communication Coverage with Connection Preservation
May 18, 2026Z. JiangMulti-Robot Systems Proximal Policy Optimization
Zhejiang University
Heterogeneous Information-Bottleneck Coordination Graphs for Multi-Agent Reinforcement Learning
May 17, 2026Wei Duan, Junyu Xuan, En Yu +2Graph Structure Learning Information Bottleneck
Australian Artificial Intelligence Institute (AAII), University of Technology Sydney
Beyond Partner Diversity: An Influence-Based Team Steering Framework for Zero-Shot Human-Machine Teaming
May 14, 2026Wei Sheng, Rohan PalejaMulti-Agent Coordination Cooperative MARL
Department of Computer Science Purdue University
ERPPO: Entropy Regularization-based Proximal Policy Optimization
May 13, 2026Changha Lee, Gyusang ChoMulti-Agent Systems Proximal Policy Optimization
Korea Advanced Institute of Science and Technology, Daejeon, South Korea
Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning
May 12, 2026Wo Wei Lin, Ethan Rathbun, Enrico Marchesini +1Reinforcement Learning Robust RL
Department of Computer Sciences, Northeastern University, United States · Department of Computer Sciences, Massachusetts Institute of Technology, United States
Events as Triggers for Behavioral Diversity in Multi-Agent Reinforcement Learning
May 12, 2026Hannes Büchi, Manon Flageat, Eduardo Sebastián +1Behavioral Diversity in RL Cooperative MARL
Department of Computer Science and Technology, University of Cambridge, UK
Adaptive TD-Lambda for Cooperative Multi-agent Reinforcement Learning
May 12, 2026Yue Deng, Zirui Wang, Yin ZhangTemporal-Difference Learning Density Ratio Estimation
1Zhongguancun Academy, Beijing, China · College of Computer Science and Technology, Zhejiang University
Shaping Zero-Shot Coordination via State Blocking
May 12, 2026Mingu Kang, Sunwoo Lee, Yonghyeon Jo +1Multi-Agent Coordination Cooperative MARL
Graduate School of Artificial Intelligence UNIST Ulsan, South Korea 44919
PC3D: Zero-Shot Cooperation Across Variable Rosters via Personalized Context Distillation
May 11, 2026Ahmet Onur Akman, Rafał KucharskiZero-Shot Learning Decentralized MARL
Doctoral School of Exact and Natural Sciences, Jagiellonian University, Kraków, Poland · Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
A Cross-Layered Multi-Drone Coordination for Medical Supply Delivery during Disaster Response Management
May 10, 2026Aneesh Calyam, Subrahmanya Chandra Bhamidipati, Zack Murry +1Aerial Robotics UAV Navigation
University of Missouri–Columbia, Columbia, MO 65211, USA
Rethinking Ratio-Based Trust Regions for Policy Optimization in Multi-Agent Reinforcement Learning
May 9, 2026Chulabhaya Wijesundara, Andrea Baisero, Zhongheng Li +3Multi-Agent System Optimization Policy Optimization
Northeastern University · STR · University of California, Irvine
SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning
May 8, 2026Nikunj Gupta, James Zachary Hare, Jesse Milzman +2Multi-Agent Coordination Partially Observable RL
University of Southern California, Los Angeles, CA, USA · DEVCOM Army Research Laboratory, Adelphi, MD, USA · DEVCOM Army Research Office, Research Triangle Park, North Carolina, USA
The Reciprocity Gradient
May 8, 2026Yue Lin, Pascal Poupart, Shuhui Zhu +5Credit Assignment in RL Policy Gradient Methods
The Chinese University of Hong Kong, Shenzhen · LIGHTSPEED · University of Waterloo +2
Decentralized Diffusion Policy Learning for Enhanced Exploration in Cooperative Multi-agent Reinforcement Learning
May 8, 2026Yuyang Zhang, Haldun Balim, Na LiDiffusion Policy Decentralized MARL
SEAS, Harvard University
Randomness is sometimes necessary for coordination
May 7, 2026Rohan Patil, Jai Malegaonkar, Henrik I. ChristensenMulti-Agent Coordination Multi-Agent Systems
Coordination Matters: Process-Aware 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 Zero-Shot Policy Transfer
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

May 18, 2026J/K move · Enter open · S save

Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning

Sunwoo Lee, Mingu Kang, Yonghyeon Jo, Seungyul Han

Graduate School of Artificial Intelligence, UNIST, Ulsan, South Korea

Adversarial Training Multi-Agent Coordination Adversarial MARL Robust RL Cooperative MARL Multi-Agent Reinforcement Learning

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Cooperation is central to multi-agent reinforcement learning (MARL), yet learned coordination can be fragile when external perturbations disrupt inter-agent interactions. Prior robust MARL methods have primarily considered value-oriented attacks, leaving a gap in robustness when interaction structures themselves are corrupted. In this paper, we propose an interaction-breaking adversarial learning (IBAL) framework that takes an information-theoretic view to construct attacks that impede coordination by perturbing agents' observations and actions, and trains agents to perform reliably under such disruptions. Empirically, our approach improves robustness over existing robust MARL baselines across diverse attack settings and yields stronger performance even under agent-missing scenarios. Our code is available at https://sunwoolee0504.github.io/IBAL.