Cooperative MARL

MARL: Multi-Agent Reinforcement Learning

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Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning
Jul 7, 2026Timothée Gavin, Murat BronzRobotic RL Cooperative MARL
ENAC-LAB, LAAS-RIS · *IAS, Thales LAS, Rungis, France · †Dynamic Systems, OPTIM, Fédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse, Toulouse, France +2
Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids
Jul 6, 2026Omar Al-Refai, Ibrahim Shahbaz, Adam Ali Husseinat +1Reinforcement Learning Federated RL
iSTAR Lab, Texas A&M University College Station, TX, USA
Multi-Robot Open Adaptive Teaming Across Unseen Environments, Partners, and Scales
Jul 6, 2026Yang Li, Feng Xue, Fan Mo +6Robot Policy Adaptation Multi-Robot Systems
1Shanghai Jiao Tong University · 2Sun Yat-sen University · 3National University of Singapore +4
Integrated Altruistic and Fairness Preference Induces Advanced Mutual Cooperation in Sequential Social Dilemmas
Jul 6, 2026Yu Wei, Yukiko Ogura, Yoshiyuki Ohmura +3Social Dilemmas Cooperative MARL
The University of Tokyo
Regime-Conditional Stabilisation of LLM-Augmented Cooperative Multi-Agent Reinforcement Learning
Jul 5, 2026Faid Keddouri, Sohaib Houhou, Aissa Boulmerka +1Reward Shaping LLM-Guided RL
National School of Artificial Intelligence (ENSIA), Sidi Abdellah Campus, Algiers, Algeria · Cosys-Grettia, Univ Gustave Eiffel, F-77454 Marne-la-Vallee, France.
MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination
Jul 3, 2026Rui Zuo, Qinwei Huang, Mingyang Li +3Multi-Agent System Optimization Multi-Agent Coordination
Syracuse University · Air Force Research Laboratory
Simulation Based Reward Function Validation for Multi-Agent On Orbit Inspection
Jul 1, 2026Patrick Quinn, Bala Prenith Reddy Gopu, George M. Nehma +1Robotic Inspection Cooperative MARL
Florida Institute of Technology, Melbourne, FL., 32901
Distributed Multi Robot Lunar Cargo Transportation via Phase Decomposed Reinforcement Learning
Jun 30, 2026Ashutosh Mishra, Elian Neppel, Shreya Santra +4Multi-Robot Systems Space Robotics
Space Robotics Lab. (SRL), Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University, Sendai 980–8579, Japan · École Centrale de Lille, France · Institut Teknologi Bandung, Indonesia
Sampling-Based Coordination-Informed Multi-Objective Multi-Robot Reinforcement Learning
Jun 29, 2026Antonio Marino, Esteban Restrepo, Soon-jo Chung +2Multi-Objective Reinforcement Learning Multi-Robot Coordination
University of Cambridge · CNRS, Univ Rennes, Inria, IRISA · Division of Engineering and Applied Science, California Institute of Technology
ACPO: Agent-Chained Policy Optimization for Multi-Agent Reinforcement Learning
Jun 29, 2026Daiki E. Matsunaga, Junho Na, Tri Wahyu Guntara +4Multi-Agent System Optimization Multi-Agent Coordination
1KAIST · 2KRAFTON · University of Toronto +2
Learned Coordination Conventions in Cooperative MARL: Measuring the Translation Gap Between Theory-Informed Roles and Learned Routing
Jun 28, 2026Yoosung HongMulti-Agent Coordination Cooperative MARL
Independent Researcher
HiComm: Hierarchical Communication for Multi-agent Reinforcement Learning
Jun 28, 2026Runze Zhao, Dongruo Zhou, Sumit Kumar Jha +2Multi-Agent Communication Cooperative MARL
Luddy School of Informatics, Computing, and Engineering Indiana University Bloomington Bloomington, IN 47408 · Department of Computer & Information Science & Engineering University of Florida Gainesville, FL 32611 · Department of Electrical Engineering and Computer Science United States Military Academy West Point, NY 10996 +1
Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning
Jun 24, 2026Bang Giang Le, Viet Cuong TaSequential MARL Trust Region Policy Optimization
Human Machine Interaction Laboratory, VNU University of Engineering and Technology, 144 Xuan Thuy, Cau Giay, 100000, Hanoi, Vietnam.
Offline Multi-agent Continual Cooperation via Skill Partition and Reuse
Jun 24, 2026Yuchen Xiao, Lei Yuan, Ruiqi Xue +2Offline RL Agent Skill Learning
National Key Laboratory for Novel Software Technology, Nanjing University · School of Artificial Intelligence, Nanjing University · Polixir Technologies +1
GCT-MARL: Graph-Based Contrastive Transfer for Sample-Efficient Cooperative Multi-Agent Reinforcement Learning
Jun 23, 2026Animesh Animesh, Satheesh K Perepu, Kaushik DeyGraph Contrastive Learning Contrastive RL
Department of Artificial Intelligence, Indian Institute of Technology Kharagpur, India · Work done during research internship at Ericsson Research, Bangalore, India · Ericsson Research, Bangalore, India
ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning
Jun 23, 2026Anurag Akula, Satheesh K. Perepu, Abhishek Sarkar +1Cross-Domain Transfer Learning State Representation Learning
Indian Institute of Technology Madras, India · Ericsson Research, India
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
Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition
Jun 19, 2026Bingchang Song, Yiqin YangMulti-Agent Coordination Cooperative MARL
Department of Computer Science and Engineering, Washington University in St. Louis · The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
BARD-MARL: Byzantine-Agent Detection for Learned Communication in Multi-Agent Reinforcement Learning
Jun 15, 2026Almond Kiruthu MurimiMulti-Agent Systems Multi-Agent System Security
Carnegie Mellon University Department of Electrical and Computer Engineering Pittsburgh, Pennsylvania, United States
Learning Coordinated Preference for Multi-Objective Multi-Agent Reinforcement Learning
Jun 12, 2026Pengxin Wang, Lihao Guo, Yi Xie +3Multi-Objective Reinforcement Learning Multi-Agent System Optimization
Department of Electrical and Computer Engineering, University of Arizona
Temporally Consistent Graph Q-Networks for Intelligent Network Control
Jun 11, 2026Zacharias Veiksaar, Maxime BoutonCooperative MARL Multi-Agent Reinforcement Learning
Work done while at Ericsson Research, Sweden. · Ericsson Research, Sweden.
Safety-Contract Graph Multi-Agent Reinforcement Learning for Autonomous Network Security Response
Jun 11, 2026Jose Luis Lima de Jesus SilvaAutonomous Cyber Defense Risk-Sensitive RL
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
$α$ -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
CCKS: Consensus-based Communication and Knowledge Sharing
Jun 10, 2026Jinyuan Zu, Xiaowei Lv, Yongcai Wang +5Multi-Agent Consensus Decentralized MARL
School of Information, Renmin University of China, Beijing 100872, China · State Key Laboratory of Multimodal Artificial Intelligence Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · The Information Science Academy, China Electronics Technology Group Corporation, Beijing 100043, China +1
Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibria
Jun 9, 2026Wongyu Lee, Francesco Lelli, Omran Ayoub +1Multi-Agent Coordination Regret Minimization
Politecnico di Milano · Tilburg University · University of Applied Sciences and Arts of Southern Switzerland
Cooperative Long Rope Skipping via Multi-Agent Reinforcement Learning
Jun 6, 2026Zihao Wang, Shijie Peng, Kerui Wu +6Cooperative MARL Humanoid Robotics
National Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China · School of Artificial Intelligence, Nanjing University, Nanjing, China · Beijing Academy of Artificial Intelligence, BAAI, Beijing, China
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
Learning to Contest: Decentralized Robust Fairness in Cooperative MARL via Cross-Attention
Jun 4, 2026Can SavcıRobust RL Cooperative MARL
Independent Researcher Izmir, Turkey
Merging model-based control with multi-agent reinforcement learning for multi-agent cooperative teaming strategies
Jun 4, 2026Christian Llanes, Spencer W. Jensen, Samuel CooganMulti-Agent Control Multi-Robot Coordination
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. · Sandia National Laboratories, Albuquerque, NM 87123, USA.
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

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Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning

Timothée Gavin, Murat Bronz

ENAC-LAB, LAAS-RIS · *IAS, Thales LAS, Rungis, France · †Dynamic Systems, OPTIM, Fédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse, Toulouse, France+2

Robotic RL Cooperative MARL Multi-Agent Reinforcement Learning

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This article presents a solution to intercept an agile drone by a team of agile drone carrying catching nets. We formulate the problem as a competitive Multi-Agent Reinforcement Learning (MARL) task. To address the problem of nonstationarity and catastrophic forgetting of agents overfitting to the current opponent strategy, we train the pursuers and the evader using Multi-Agent Proximal Policy Optimization (MAPPO) with Prioritized Fictitious Self Play (PFSP). We train the agents in a high-fidelity simulator using low-level control commands, collective thrust and body rates (CTBR), to achieve agile flights for both the pursuers and the evader. We compare the performance of the trained policies in terms of catch rate, time to catch and crash rates, against heuristic baselines and show that our solution outperforms them. Ablation studies show that PFSP lead to more robust policies that can adapt to different opponent strategies, and that a low-level control commands are crucial for learning performing strategies in the pursuit-evasion task. Finally, a qualitative analysis of the learned behaviours highlights the emergence of cooperative tactics among the pursuers.

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