Constrained MARL

MARL: Multi-Agent Reinforcement Learning

Follow this topic Ask about this topic

Latest papers

14 papers4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Vulnerability Analysis for Safe Multi-Agent Reinforcement
Oct 19, 2026Taozheng Zhu, Shixiong Jiang, Fanxin KongAdversarial Attacks Adversarial MARL
University of Notre Dame, Notre Dame, Indiana, USA
Constrained Multi-Agent Reinforcement Learning in Imagination
Oct 15, 2026Raz Shmueli, Shahaf S. ShperbergConstrained MARL Multi-Agent Reinforcement Learning
Ben-Gurion University of the Negev, Beer-Sheva, Israel
SafeDiSTL: Decomposing Joint Constraints for Safe Multi-Agent Reinforcement Learning
Oct 9, 2026Hongrui Zheng, Aditya Singh, Rahul MangharamConstrained MARL Multi-Agent Reinforcement Learning
University of Pennsylvania, Philadelphia, USA
Who Bears the Burden? Learning Responsibility for Shared Constraints in Multi-Agent Reinforcement Learning
Oct 5, 2026Xiaoyang Cao, Jingqi Li, Zhe Fu +1Game Theory Constrained MARL
Massachusetts Institute of Technology · The University of Texas at Austin · Stanford University +1
Learning to Harvest Without Collapse in a Regenerative Commons: A Lagrangian Framework
Sep 29, 2026Jose Tupayachi, Xueping Li, Soham DasConstrained MARL Multi-Agent Control
Oak Ridge National Laboratory Department of Industrial and Systems Engineering, University of Tennessee, Knoxville · Department of Industrial and Systems Engineering, University of Tennessee, Knoxville
Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control
Jun 22, 2026Zihao Guo, Jianing Zhao, Ling Li +3Robotic Control Multi-Agent Coordination
1King’s College London · University of California, Berkeley · 3The Alan Turing Institute
A Multi-Agent system for Multi-Objective constrained optimization
Jun 18, 2026Federica FilippiniReward Shaping Multi-Agent System Optimization
University of Milano-Bicocca Milan, Italy
TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning
Jun 16, 2026Zijie Meng, Ziwei Li, Yufei Liu +5Cyber-Physical Systems Reinforcement Learning
Peking University · National Taiwan University · WHU +1
Safety-Contract Graph Multi-Agent Reinforcement Learning for Autonomous Network Security Response
Jun 11, 2026Jose Luis Lima de Jesus SilvaAutonomous Cyber Defense Constrained MARL
Oxaala Tecnologias, Rua Dinah Silveira de Queirós, 06, Quinta do Candeal, Horto Florestal, Salvador, 40296-160, Bahia, Brazil · Universidade Federal da Bahia, Instituto de Geociências, Rua Barão de Jeremoabo, s/n, Ondina, Salvador, Bahia, 40170-115, Brazil
Coordination Graphs for Constrained Multi-Agent Reinforcement Learning
Jun 1, 2026Santiago Amaya-Corredor, Miguel Calvo-Fullana, Anders JonssonMulti-Objective Reinforcement Learning Multi-Agent System Optimization
Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain
Constrained Multi-Objective Reinforcement Learning with Max-Min Criterion
May 29, 2026Giseung Park, Hyunyoung Nam, Woohyeon Byeon +2Multi-Objective Reinforcement Learning Reinforcement Learning
Robotics Institute, University of Toronto, 55 St George St, Toronto, Canada. · School of Electical Engineering, KAIST, Daejeon 34141, Republic of Korea. · Faculty of Engineering, Bar-Ilan University, Ramat Gan 52900, Israel.
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
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
Safe Multi-Agent Deep Reinforcement Learning for Privacy-Aware Edge-Device Collaborative DNN Inference
Feb 23, 2026Hong Wang, Xuwei Fan, Zhipeng Cheng +4Constrained MARL Efficient Neural Network Inference
School of Future Science and Engineering, Soochow University, Suzhou 215006, China · College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China · School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China +2

Oct 19, 2026J/K move · Enter open · S save

Vulnerability Analysis for Safe Multi-Agent Reinforcement

Taozheng Zhu, Shixiong Jiang, Fanxin Kong

University of Notre Dame, Notre Dame, Indiana, USA

Adversarial Attacks Adversarial MARL Multi-Agent System Security Multi-Agent Reinforcement Learning Constrained MARL

Open paper AskPDF arXiv

Safe Multi-Agent Reinforcement Learning (SMARL) aims to learn cooperative control policies under prescribed safety constraints. Al- though existing SMARL methods focus on enforcing safety during policy optimization, their vulnerability remains largely unexplored. This paper investigates inference-time observation attacks against SMARL, where an adversary perturbs the observations of a subset of agents, thereby altering the actions produced by their policies. We propose MinMax Attack, an attack framework that jointly selects adversarial target actions for compromised agents while accounting for the Cooperative Resistance of benign agents. We further derive a bound on the induced safety cost and characterize its relation- ship with the Compromised-Agent Budget and the Perturbation Budget. Experiments across multiple safety-critical multi-agent benchmarks and representative state-of-the-art SMARL algorithms show that MinMax Attack induces substantially higher safety costs than existing attack baselines while preserving task reward, thereby reducing its detectability and achieving both attack effectiveness and stealthiness.