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.

MARL-GPT: Foundation Model for Multi-Agent Reinforcement Learning
Apr 7, 2026Maria Nesterova, Mikhail Kolosov, Anton Andreychuk +6Multi-Agent Reinforcement Learning Transformer-Based RL
AXXX & MIRAI, Moscow, Russia · MIRAI, Moscow, Russia · AXXX, Moscow, Russia +2
Breakthrough the Suboptimal Stable Point in Value-Factorization-Based Multi-Agent Reinforcement Learning
Apr 7, 2026Lesong Tao, Yifei Wang, Haodong Jing +4Multi-Agent Reinforcement Learning Cooperative MARL
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · Zhongguancun Academy
Learning to Focus: CSI-Free Hierarchical MARL for Reconfigurable Reflectors
Apr 6, 2026Hieu Le, Mostafa Ibrahim, Oguz Bedir +2Reconfigurable Intelligent Surfaces Hierarchical RL
Electrical and Computer Engineering Texas A&M University College Station, Texas, USA · Engineering Technology and Industrial Distribution Texas A&M University College Station, Texas, USA · School of Performance, Visualization, and Fine Arts Texas A&M University College Station, Texas, USA
Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning
Apr 6, 2026Yiyao Zhang, Diksha Goel, Hussain AhmadAdversarial MARL Autonomous Cyber Defense
Adelaide University, Australia · CSIRO’s Data61, Australia
Finite-Time Analysis of Q-Value Iteration for General-Sum Stackelberg Games
Apr 6, 2026Narim Jeong, Donghwan LeeStackelberg Games Game-Theoretic RL
Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
Territory Paint Wars: Diagnosing and Mitigating Failure Modes in Competitive Multi-Agent PPO
Apr 4, 2026Diyansha SinghMulti-Agent Reinforcement Learning Self-Play RL
Independent Researcher
Investigating the Impact of Subgraph Social Structure Preference on the Strategic Behavior of Networked Mixed-Motive Learning Agents
Apr 4, 2026Xinqi Gao, Mario VentrescaMulti-Agent Systems Multi-Agent Reinforcement Learning
Xinqi Gao is with the Department of Industrial Engineering, Purdue University, West Lafayette, IN 47906, USA · Mario Ventresca is with the Department of Industrial Engineering, Purdue University, West Lafayette, IN 47906, USA
Fully Byzantine-Resilient Distributed Multi-Agent Q-Learning
Apr 3, 2026Haejoon Lee, Dimitra PanagouMulti-Agent Reinforcement Learning Distributed RL
Department of Robotics, University of Michigan, Ann Arbor, MI, USA
GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning
Apr 3, 2026Ornith Team, Xiaoya Li, Guoyin Wang +3RL for Code Generation Agentic RL
DeepReinforce Team
Collaborative Task and Path Planning for Heterogeneous Robotic Teams using Multi-Agent PPO
Apr 1, 2026Matthias Rubio, Julia Richter, Hendrik Kolvenbach +1Multi-Robot Task Allocation Multi-Robot Motion Planning
Robotic Systems Lab (RSL), ETH Zürich, Zürich, Switzerland
LangMARL: Natural Language Multi-Agent Reinforcement Learning
Apr 1, 2026Huaiyuan Yao, Longchao Da, Xiaoou Liu +3RL for LLM Agents Multi-Agent Reinforcement Learning
Arizona State University · Cisco Research · University of North Carolina at Chapel Hill
Internal State-Based Policy Gradient Methods for Partially Observable Markov Potential Games
Apr 1, 2026Wonseok Yang, Thinh T. DoanNatural Policy Gradient Multi-Agent Reinforcement Learning
University of Texas at Austin, Austin, TX, 78712 USA
Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback
Mar 30, 2026Andi Nika, Debmalya Mandal, Parameswaran Kamalaruban +2Multi-Agent Reinforcement Learning Offline MARL
MPI-SWS · University of Warwick · Visa
SkyNet: Belief-Aware Planning for Partially-Observable Stochastic Games
Mar 29, 2026Adam HailePartially Observable Stochastic Games Imperfect-Information Games
Dwight and Dian Diercks School of Advanced Computing Milwaukee School of Engineering 1025 N Broadway St, Milwaukee, WI 53202
Multi-AUV Ad-hoc Networks-Based Multi-Target Tracking Based on Scene-Adaptive Embodied Intelligence
Mar 28, 2026Kai Tian, Jialun Wang, Chuan Lin +4Multi-Object Tracking Underwater Robotics
Software College, Northeastern University, Shenyang, China. · Key Laboratory of Maritime Intelligent Network Information Technology, Ministry of Education, Hohai University.
Learning Rollout from Sampling:An R1-Style Tokenized Traffic Simulation Model
Mar 26, 2026Ziyan Wang, Peng Chen, Ding Li +4Autonomous Driving Simulation Reinforcement Learning
State Key Laboratory of Intelligent Transportation System, Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology, School of Transportation Science and Engineering, Beihang University, Beijing 100191, China. · State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
COIN: Collaborative Interaction-Aware Multi-Agent Reinforcement Learning for Self-Driving Systems
Mar 26, 2026Yifeng Zhang, Jieming Chen, Tingguang Zhou +4Centralized Training with Decentralized Execution Multi-Agent Coordination
Department of Mechanical Engineering, College of Design and Engineering, National University of Singapore, 21 Lower Kent Ridge Rd, 119077, Singapore · Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, 100872, China · School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China
CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control
Mar 25, 2026Yifeng Zhang, Harsh Goel, Peizhuo Li +3Traffic Signal Control Multi-Agent Coordination
Department of Mechanical Engineering, National University of Singapore · Chandra Department of Electrical and Computer Engineering, The University of Texas at Austin · Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
LATS: Large Language Model Assisted Teacher-Student Framework for Multi-Agent Reinforcement Learning in Traffic Signal Control
Mar 25, 2026Yifeng Zhang, Peizhuo Li, Tingguang Zhou +2Traffic Signal Control LLM-Guided RL
Department of Mechanical Engineering, National University of Singapore
Large Language Model Guided Incentive Aware Reward Design for Cooperative Multi-Agent Reinforcement Learning
Mar 25, 2026Dogan Urgun, Gokhan GungorReward Design for RL Cooperative MARL
Department of Electrical and Electronics Engineering, Karabuk University, 78050 Karabuk, Türkiye · Department of Mechatronics Engineering, Karabuk University, 78050 Karabuk, Türkiye
Partial Attention in Deep Reinforcement Learning for Safe Multi-Agent Control
Mar 23, 2026Turki Bin Mohaya, Peter SeilerRL for Autonomous Driving Multi-Agent Reinforcement Learning
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA.
Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications
Mar 23, 2026Che Chen, Lanhua Li, Shimin Gong +3Spatio-Temporal Attention Multi-Agent Reinforcement Learning
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518000, China, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China · Department of Equipment Management and Unmanned Aerial Vehicle Engineering, Air Force Engineering University, Xi’an · School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics, Nanchang 330032, China +1
Adaptive Robust Estimator for Multi-Agent Reinforcement Learning
Mar 23, 2026Zhongyi Li, Wan Tian, Jingyu Chen +8Multi-Agent Reinforcement Learning RL for Language Model Reasoning
Beihang University · Peking University · Chinese Academy of Sciences +1
CAMA: Exploring Collusive Adversarial Attacks in c-MARL
Mar 20, 2026Men Niu, Xinxin Fan, Quanliang Jing +2Adversarial Attacks Adversarial MARL
Institute of Computing Technology, CAS · UCAS · Beihang University
Bounded Coupled AI Learning Dynamics in Tri-Hierarchical Drone Swarms
Mar 20, 2026Oleksii BychkovSwarm Robotics Multi-Agent Reinforcement Learning
Taras Shevchenko National University of Kyiv
Emergency Preemption Without Online Exploration: A Decision Transformer Approach
Mar 20, 2026Haoran Su, Hanxiao Deng, Yandong SunTraffic Signal Control Decision Transformer
New York University · University of California, Berkeley
MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery
Mar 19, 2026Dong Li, Zhengzhang Chen, Xujiang Zhao +5Causal Discovery Causal Structure Learning
Department of Computer Science, Baylor University · NEC Labs America · School of Computer and Cyber Sciences, Augusta University +2
MA-VLCM: A Vision Language Critic Model for Value Estimation of Policies in Multi-Agent Team Settings
Mar 16, 2026Shahil Shaik, Aditya Parameshwaran, Anshul Nayak +2Multi-Agent Reinforcement Learning VLMs for Robotics
Mechanical Engineering Department, Clemson University. · Ground Vehicle Systems Center U.S. Army CCDC
EcoFair-CH-MARL: Scalable Constrained Hierarchical Multi-Agent RL with Real-Time Emission Budgets and Fairness Guarantees
Mar 15, 2026Saad AlqithamiConstrained MARL Multi-Agent Reinforcement Learning
Computer Science Department, Al-Baha University, Albaha 65779, Saudi Arabia
Joint Optimization of Multi-agent Memory System
Mar 13, 2026Wenyu Mao, Haoyang Liu, Haosong Tan +4Memory-Augmented Language Model Agents Multi-Agent System Optimization
University of Science and Technology of China · Institute of Dataspace, Hefei Comprehensive National Science Center

Apr 7, 2026J/K move · Enter open · S save

MARL-GPT: Foundation Model for Multi-Agent Reinforcement Learning

Maria Nesterova, Mikhail Kolosov, Anton Andreychuk, Egor Cherepanov+5

AXXX & MIRAI, Moscow, Russia · MIRAI, Moscow, Russia · AXXX, Moscow, Russia+2

Multi-Agent Reinforcement Learning Transformer-Based RL Offline MARL

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Recent advances in multi-agent reinforcement learning (MARL) have demonstrated success in numerous challenging domains and environments, but typically require specialized models for each task. In this work, we propose a coherent methodology that makes it possible for a single GPT-based model to learn and perform well across diverse MARL environments and tasks, including StarCraft Multi-Agent Challenge, Google Research Football and POGEMA. Our method, MARL-GPT, applies offline reinforcement learning to train at scale on the expert trajectories (400M for SMACv2, 100M for GRF, and 1B for POGEMA) combined with a single transformer-based observation encoder that requires no task-specific tuning. Experiments show that MARL-GPT achieves competitive performance compared to specialized baselines in all tested environments. Thus, our findings suggest that it is, indeed, possible to build a multi-task transformer-based model for a wide variety of (significantly different) multi-agent problems paving the way to the fundamental MARL model (akin to ChatGPT, Llama, Mistral etc. in natural language modeling).