Multi-Agent Reinforcement Learning

Also known as MARL

Ask about this topic

Overview

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

CardsList

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

  1. TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination

    May 1, 2026Yi Xie, Siao Liu, Falong Fan +3Multi-Agent System OptimizationMulti-Agent Coordination

    Department of Electrical & Computer Engineering, University of Arizona · Future Science and Engineering College, Soochow University · INSAIT, Sofia University ”St. Kliment Ohridski” +1

  2. Separation Assurance between Heterogeneous Fleets of Small Unmanned Aerial Systems via Multi-Agent Reinforcement Learning

    May 1, 2026Iman Sharifi, Hyeong Tae Kim, Maheed Hatem Ahmed +2Multi-Agent CoordinationMulti-Agent Reinforcement Learning

    Department of Mechanical and Aerospace Engineering, George Washington University, Washington, DC, USA · Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA

  3. Topology-Driven Anti-Entanglement Control for Soft Robots

    May 1, 2026Haoyang Le, Shengxuan Wang, Mohan Chen +1Multi-Robot CoordinationSoft Robotics

    School of Mathematics and Statistics, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, 450001, Henan, China · School of Computer Science and Artificial Intelligence, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, 450001, Henan, China · College of Information Engineering, North China University of Water Resources and Electric Power, No. 136 Jinshui East Road, Jinshui District, Zhengzhou, 450046, Henan, China

  4. Pessimism-Free Offline Learning in General-Sum Games via KL Regularization

    Apr 30, 2026Claire Chen, Yuheng ZhangNash EquilibriumReinforcement Learning

    The Division of Physics, Mathematics and Astronomy California Institute of Technology · Department of Computer Science University of Illinois Urbana-Champaign

  5. A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication

    Apr 28, 2026Valentin Cuzin-Rambaud, Laetitia Matignon, Maxime MorgeMulti-Agent CommunicationMulti-Agent Reinforcement Learning

    LIRIS, UCBL · Université Lyon 1, INSA Lyon, CNRS, LIRIS, UMR 5205, Lyon, France

  6. Zero Shot Coordination for Sparse Reward Tasks with Diverse Reward Shapings

    Apr 28, 2026Keenan Powell, Peihong Yu, Pratap TokekarMulti-Agent CoordinationReward Shaping

    Department of Computer Science University of Maryland, College Park

  7. An Analysis of the Coordination Gap between Joint and Modular Learning for Job Shop Scheduling with Transportation Resources

    Apr 27, 2026Moritz Link, Jonathan Hoss, Noah KlarmannMulti-Agent CoordinationCombinatorial Optimization

    Faculty of Management and Engineering, University of Applied Sciences, 83024 Rosenheim, Germany

  8. Non-Stationarity Breaks Permutation Surrogates in Multi-Agent Reinforcement Learning: Diagnosis and Remedies

    Apr 26, 2026Nikolaos Al. Papadopoulos, Konstantinos E. PsannisPermutation TestingMutual Information Estimation

    Department of Applied Informatics, University of Macedonia, Egnatia 156, 54636 Thessaloniki, Greece

  9. DLM: Unified Decision Language Models for Offline Multi-Agent Sequential Decision Making

    Apr 26, 2026Zhuohui Zhang, Bin Cheng, Bin HeSequential MARLOffline RL

    Department of Control Science and Engineering, Tongji University, Shanghai 201804, China · Shanghai Institute of Intelligent Science and Technology; Shanghai Research Institute for Intelligent Autonomous Systems; State Key Laboratory of Autonomous Intelligent Unmanned Systems, Shanghai 200120, China

  10. CODA: Coordination via On-Policy Diffusion for Multi-Agent Offline Reinforcement Learning

    Apr 25, 2026Marcel Hedman, Kale-ab Abebe Tessera, Juan Claude Formanek +5Multi-Agent CoordinationOffline RL

    Inephany Ltd · Department of Statistics, University of Oxford · The University of Edinburgh +2

  11. A Hierarchical MARL-Based Approach for Coordinated Retail P2P Trading and Wholesale Market Participation of DERs

    Apr 22, 2026Patrick Wilk, Ethan Cantor, Yikui Liu +1Multi-Agent Reinforcement LearningPower Systems

    aDepartment Electrical and Computer Engineering, Rowan University, 201 Mullica Hill Rd, Glassboro, New Jersey, 08028, United States · bSchool of Electrical Engineering, Sichuan University, 24 South Section 1, 1st Ring Road, Chengdu, 610065, China

  12. Quantum Frog: Emergent Cooperation and Difficulty Scaling in a Quantized-Time Cooperative Game

    Apr 22, 2026Saad MankariousMulti-Agent CoordinationGame-Playing Agents

  13. Scalable Neighborhood-Based Multi-Agent Actor-Critic

    Apr 20, 2026Tim Goppelsroeder, Rasmus JensenMulti-Agent SystemsActor-Critic Methods

    Department of Computer Science, ETH Zürich.

  14. COSAC: Counterfactual Credit Assignment in Sequential Cooperative Teams

    Apr 20, 2026Shripad Deshmukh, Jayakumar Subramanian, Raghavendra Addanki +1Sequential MARLCredit Assignment in RL

    College of Information and Computer Sciences University of Massachusetts Amherst · Adobe Research San Jose, CA

  15. 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 +1Multi-Agent Reinforcement LearningMulti-Agent Coordination

    Technical University of Munich (TU Munich) · Brown University · Indian Institute of Technology Tirupati (IIT Tirupati)

  16. MARS2^2: Scaling Multi-Agent Tree Search via Reinforcement Learning for Code Generation

    Apr 16, 2026Pengfei Li, Shijie Wang, Fangyuan Li +7RL for Code GenerationMulti-Agent Collaboration

    Shanghai Artificial Intelligence Laboratory · Shanghai Innovation Institute · Harbin Institute of Technology +1

  17. Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach

    Apr 15, 2026Li-Hsiang Shen, Yu-Quan ZhengHybrid Action Space RLWireless Communications

    Department of Communication Engineering, National Central University, Taoyuan 320317, Taiwan

  18. Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus

    Apr 15, 2026Zijian Zhao, Jing Gao, Sen LiCooperative MARLMulti-Agent Reinforcement Learning

    1The Hong Kong University of Science and Technology · 2The Hong Kong Polytechnic University · 3The Hong Kong University of Science and Technology (Guangzhou)

  19. OOM-RL: Out-of-Money Reinforcement Learning Market-Driven Alignment for LLM-Based Multi-Agent Systems

    Apr 13, 2026Kun Liu, Liqun ChenAI AlignmentRL for LLM Agents

  20. Load constrained wind farm flow control through multi-objective multi-agent reinforcement learning

    Apr 13, 2026Teodor Åstrand, Marcus Binder Nilsen, Iasonas Tsaklis +3Multi-Agent Reinforcement LearningConstrained RL

    Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark

  21. MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments

    Apr 13, 2026Abhishek Sawaika, Samuel Yen-Chi Chen, Udaya Parampalli +1Quantum RLDistributed RL

    Quantum Cloud Computing and Distributed Systems Lab (qCLOUDS), School of Computing and Information Systems, The University of Melbourne, Australia · Brookhaven National Laboratory, Upton, NY, USA

  22. EvoNash-MARL: A Closed-Loop Multi-Agent Reinforcement Learning Framework for Medium-Horizon Equity Allocation

    Apr 13, 2026Chongliu Jia, Yi Luo, Sipeng Han +5Evolutionary RLPortfolio Optimization

    University of Connecticut · Xi’an University of Technology · Iowa State University +1

  23. A Dual-Positive Monotone Parameterization for Multi-Segment Bids and a Validity Assessment Framework for Reinforcement Learning Agent-based Simulation of Electricity Markets

    Apr 11, 2026Zunnan Xu, Zhaoxia Jing, Zhanhua PanAutomated BiddingConstrained RL

    School of Electric Power Engineering, South China University of Technology, Guangzhou, 510640, Guangdong, China · Department of Engineering, University of Exeter, Exeter EX4 4PY, U.K.

  24. NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions

    Apr 10, 2026Igor JankowskiAutonomous Cyber DefenseCybersecurity

  25. Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks

    Apr 10, 2026Wen Qiu, Zhiqiang He, Wei Zhao +1UAV CommunicationReinforcement Learning

    Department of Information and Communication Engineering, Kitami Institute of Technology, Japan. · Graduate School of Informatics and Engineering, the University of Electro-Communications, Japan. · School of Computer Science and Technology, Anhui University of Technology, China. +1

  26. Multi-agent Reinforcement Learning for Low-Carbon P2P Energy Trading among Self-Interested Microgrids

    Apr 10, 2026Junhao Ren, Honglin Gao, Lan Zhao +3Power SystemsMulti-Agent Reinforcement Learning

    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · School of Information Science and Technology, University of Science and Technology of China, Hefei, China · Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research, Singapore

  27. Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning

    Apr 9, 2026Diyi Hu, Bhaskar KrishnamachariMulti-Agent Reinforcement LearningCooperative MARL

    University of Southern California

  28. Intelligent Three Level Learning Architecture for Autonomous UAV Swarms in Search and Rescue

    Apr 9, 2026Oleksii BychkovSwarm RoboticsCognitive Architectures for AI Agents

    Department of Program Systems and Technologies, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine

  29. Joint Task Offloading, Inference Optimization and UAV Trajectory Planning for Generative AI Empowered Intelligent Transportation Digital Twin

    Apr 9, 2026Xiaohuan Li, Junchuan Fan, Bingqi Zhang +3Intelligent Transportation SystemsDigital Twins

    Guangxi University Key Laboratory of Intelligent Networking and Scenario System (School of Information and Communication, Guilin University of Electronic Technology), Guilin 541004, China · Research Institute of Highway Ministry of Transport, Beijing 100000, China · School of Automation, Guangdong University of Technology, Guangzhou 510006, China +1

  30. TwinLoop: Simulation-in-the-Loop Digital Twins for Online Multi-Agent Reinforcement Learning

    Apr 8, 2026Nan Zhang, Zishuo Wang, Shuyu Huang +4Digital TwinsMulti-Agent Reinforcement Learning

    Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China · Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China · Department of Informatics and Telecommunications, University of Thessaly, Lamia, Greece