Multi-Agent Reinforcement Learning

Also known as MARL

Multi-agent reinforcement learning studies how multiple decision-makers learn when each agent’s actions affect both the environment and other agents, across cooperative, competitive, and mixed-motive settings. Common approaches include centralized training with decentralized execution, independent or shared actor-critic policies, value factorization such as QMIX, self-play and game-solving methods, and graph-based communication or hierarchical policies. Researchers test these methods in benchmarks such as SMAC, MPE, Overcooked, and multi-agent MuJoCo, and apply them to robotics, traffic, wireless networks, energy systems, and language-agent workflows. A central challenge is scaling coordination and credit assignment while keeping learning stable under non-stationarity and policies robust, safe, and fair with unfamiliar partners and conditions.

Latest papers

373 papers47 papers in the last four weeks, up 81% on the four weeks before. 0.3% of all new papers.

$α$ -fair heterogeneous agent reinforcement learning
Jun 11, 2026Yao-hua Franck Xu, Tayeb Lemlouma, Jean-Marie Bonnin +1Algorithmic Fairness Policy Optimization
Orange Innov Lannion, France · IRISA Université de Rennes Lannion, France · IRISA IMT Atlantique Rennes, France
Individual Control Barrier Functions-Guided Diffusion Model for Safe Offline Multi-Agent Reinforcement Learning
Jun 10, 2026Qingyun Guo, Junyi Shi, Jianuo Huang +1Control Barrier Functions Offline RL
Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland · School of Computing and Data Science, Xiamen University Malaysia, Sepang, Malaysia · Department of Computer Science, University of Toronto, Toronto, Canada
Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibria
Jun 9, 2026Wongyu Lee, Francesco Lelli, Omran Ayoub +1Multi-Agent Coordination Swap Regret
Politecnico di Milano · Tilburg University · University of Applied Sciences and Arts of Southern Switzerland
Cross-Agent Learning Signals Enable Coordinated Role-Decomposed LLM Training
Jun 9, 2026Jaewan Park, Solbee Cho, Jay-Yoon LeeMulti-Agent LLM Systems Multi-Agent Collaboration
Seoul National University
Multi-agent rendezvous in fluid flows via reinforcement learning
Jun 9, 2026Bocheng Li, Jingran Qiu, Lihao ZhaoMulti-Agent Coordination Multi-Agent Reinforcement Learning
11AML, Department of Engineering Mechanics, Tsinghua University, Beijing, 100084 China. · Department of Physics, Gothenburg University, Gothenburg, 41296 Sweden.
Shape Formation for the Cooperative Transportation of Arbitrary Objects Using Multi-Agent Reinforcement Learning
Jun 8, 2026Mohamed Sayed, Wolfram Burgard, Tanja Katharina KaiserMulti-Robot Formation Control Multi-Robot Coordination
Department of Computer Science and Artificial Intelligence, University of Technology Nuremberg, Germany
Modelling Opinion Dynamics at Scale with Deep MARL
Jun 5, 2026Lukas Seier, Brandon Kaplowitz, Sebastian Towers +2Opinion Dynamics Multi-Agent Consensus
FLAIR University of Oxford United Kingdom · OWL University of Oxford United Kingdom · OUCE University of Oxford United Kingdom
Learning Multi-Agent Communication Protocol: Study on Information Entropy Efficiency in MARL
Jun 5, 2026Xinren Zhang, Zixin Zhong, Jiadong YuSequential MARL Agent Communication Protocols
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
Learn to Match: Two-Sided Matching with Temporally Extended Feedback
Jun 4, 2026Haijing Zong, Yancheng Liang, Boyang Zhou +1Multi-Agent Reinforcement Learning
Department of Economics, University of Washington, Seattle, United States · Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, United States
DNQ: Deep Nash Q-Network for Partially Observable n-Player Games
Jun 4, 2026Qintong Xie, Edward Koh, Xavier Cadet +1Nash Equilibrium Reinforcement Learning
Learning to Contest: Decentralized Robust Fairness in Cooperative MARL via Cross-Attention
Jun 4, 2026Can SavcıRobust RL Cooperative MARL
Independent Researcher Izmir, Turkey
Enhancing the MADDPG Algorithm for Multi-Agent Learning via Action Inference and Importance Sampling
Jun 3, 2026Marc Walden, Jason Liu, Shaashwath Sivakumar +2Opponent Modeling Importance Sampling
Department of Mathematics, University of California Los Angeles, Los Angeles, CA, USA
GARL: Game-Theoretic Reinforcement Learning for Multi-Agent Strategic Prioritisation
Jun 3, 2026Yuxiao Ye, Yiwen Zhang, Huiyuan Xie +2Multi-Agent LLM Systems Multi-Agent System Optimization
1Tsinghua University · 2The Hong Kong University of Science and Technology (Guangzhou)
Failure Modes of Deep Multi-Agent RL in Asynchronous Pricing: Reproducible Triggers, Trace Diagnostics, and a Partial Fix
Jun 3, 2026Shree Murthy, Rohan PandeyAsynchronous RL Algorithmic Collusion
DigitalOcean, USA
CoPark: Learning Reactive Parking via Self-Play
Jun 2, 2026Jiarong Wei, Yanxing Chen, Sinuo Song +3Multi-Agent Coordination Self-Play RL
Department of Computer Science, University of Freiburg, Germany · CARIAD SE, Germany · Technical University of Munich, Germany
Traj-Evolve: A Self-Evolving Multi-Agent System for Patient Trajectory Modeling in Lung Cancer Early Detection
Jun 1, 2026Sihang Zeng, Matthew Thompson, Ruth Etzioni +1Retrieval-Augmented Generation Medical Diagnosis
University of Washington · Fred Hutch Cancer Center · Google
Digital Twin-Assisted Adaptive Multi-Agent DRL for Intelligent Spectrum and Resource Management in Open-RAN UAV-Enabled 6G Networks
May 31, 2026Marwan Dhuheir, Thang X. Vu, Symeon ChatzinotasWireless Communications Open Radio Access Network
The Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg.
Generalized Intention Modeling in Multi-Agent Reinforcement Learning
May 29, 2026Mateusz Odrowaz-Sypniewski, Jasmine Bayrooti, Ajay Shankar +1Opponent Modeling Multi-Agent Reasoning
Department of Computer Science and Technology, University of Cambridge, UK
HADT: A Heterogeneous Multi-Agent Differential Transformer for Autonomous Earth Observation Satellite Cluster
May 29, 2026Mohamad A. Hady, Muhammad Anwar Masum, Siyi Hu +3Transformer Remote Sensing
School of Computer Science and Information Technology, Adelaide University, Adelaide, 5095, SA, Australia. · School of Electrical Engineering, Computing and Mathematical Sciences (EECMS), Curtin University, Kent St, Bentley, 6102, WA, Australia. · Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland.
Safe Equilibrium Policy Optimization for Strategic Agent Policies
May 29, 2026Karthika Arumugam, Kiran Kumar Manku, Amit DhandaMulti-Agent Reinforcement Learning Game-Playing Agents
†Amazon, USA
Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics
May 28, 2026Santiago Amaya-Corredor, Miguel Calvo-Fullana, Anders JonssonMulti-Agent System Optimization Multi-Agent Coordination
Department of Engineering, University Pompeu Fabra
Mean-Field Diffuser: Scaling Offline MARL to Thousands of Agents
May 28, 2026Wenhao Li, Xiangfeng Wang, Bo JinMulti-Agent System Optimization Mean Field Games
Tongji University · East China Normal University
Privacy-Aware Agent Collaboration for Dynamic VR Slice Management in 6G SD-RAN
May 28, 2026Khaled M. Naguib, Soumaya Cherkaoui, Mahmoud M. Elmesalawy +26G Networks Wireless Communications
Department of Computer and Software Engineering, Polytechnique Montr´eal, Montr´eal, Canada · Electronics and Communications, Helwan Univ. Cairo, Egypt
Differentiable Belief-based Opponent Shaping
May 27, 2026Aarav G Sane, Karthik Sivachandran, Rohan PalejaMulti-Agent Coordination Imperfect-Information Games
Department of Computer Science Purdue University
TRACER: Turn-level Regret Matching with Inner Reinforcement Credit for Cooperative Multi-LLM Reasoning
May 27, 2026Chusen Li, Zhou Liu, Shuigeng Zhou +1Regret Minimization Multi-Agent Reasoning
1Fudan University · 2Zhongguancun Academy · 3Academy for Advanced Interdisciplinary Studies, Peking University
Global Policy-Space Response Oracles for Two-Player Zero-Sum Games
May 27, 2026Junyu Zhang, Feihong Yang, Jian Wang +2Nash Equilibrium Game Theory
Department of Electronic Engineering, Tsinghua University, Beijing, China · Qiyuan Lab, Beijing, China
Adversarial Training for Robust Coverage Network under Worst-case Facility Losses
May 26, 2026Changhao Miao, Yuntian Zhang, Tongyu Wu +2Adversarial Training Combinatorial Optimization
State Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China · Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome, 00185, Italy · School of AI, Beijing Institute of Technology, Beijing 100081, China
UnityMAS-O: A General RL Optimization Framework for LLM-Based Multi-Agent Systems
May 26, 2026Yiqun Chen, Wei Yang, Erhan Zhang +14Multi-Agent LLM Systems Multi-Agent System Optimization
Renmin University of China · Xiaohongshu Inc.
Decoupled Delay Compensation: Enhancing Pre-trained MARL Policies via Learned Dynamics Filtering
May 25, 2026Maxim Mednikov, Oren GalRobust RL Multi-Agent Reinforcement Learning
University of Haifa, Israel
Micro-Swarm Locomotion Optimization in Dynamic Flow using Multi-Objective Multi-Agent Reinforcement Learning
May 24, 2026Josef Berman, Oren GalMulti-Objective Reinforcement Learning Multi-Agent System Optimization
The Hatter Department of Marine Technologies, Leon H. Charney School of Marine Sciences, University of Haifa, Israel.

Jun 11, 2026J/K move · Enter open · S save

$α$ -fair heterogeneous agent reinforcement learning

Yao-hua Franck Xu, Tayeb Lemlouma, Jean-Marie Bonnin, Arnaud Braud

Orange Innov Lannion, France · IRISA Université de Rennes Lannion, France · IRISA IMT Atlantique Rennes, France

Algorithmic Fairness Policy Optimization Trust Region Policy Optimization Cooperative MARL Multi-Agent Reinforcement Learning Heterogeneous MARL

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Cooperation in multi-agent systems is typically optimized through utilitarian objectives that maximize overall efficiency but fail to account for reward distribution, often resulting in inequitable "leader-follower" dynamics. While fairness-based approaches encourage pro-social behaviors where every agent benefits from cooperation, many current algorithms - including those utilizing reward shaping - break the stationarity of Markov Games or lack rigorous theoretical guarantees. This creates a critical gap between fair objective methods and theoretically safe learning frameworks. We propose a novel framework that bridges

α

-fairness with Heterogeneous-Agent Trust Region Learning (HATRL), ensuring monotonic improvement and convergence toward Nash Equilibria. Our approach leverages a fair advantage function that dynamically weights agent utilities based on their expected returns, allowing the global objective to transition from purely utilitarian efficiency to

α

-fairness welfare based on the parameter

α

. We introduce two practical algorithms,

α

-fair HATRPO and

α

-fair HAPPO, and demonstrate through experiments in sequential social dilemmas like CleanUp and CommonHarvest that they perform better than HATRL's algorithms from a utilitarian point of view while achieving socially higher outcomes.