Hierarchical MARL

MARL: Multi-Agent Reinforcement Learning

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13 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

JHiMA-WebRL: A Joint Hierarchical Multi-Agent Reinforcement Learning Framework for Long-Horizon Web Tasks
Nov 3, 2026Shanka Subhra Mondal, Jiayun WangRL for GUI Agents Long-Horizon Agent Tasks
Center for Advanced AI, Accenture
SpecMAGIC: Specification-Guided Multi-Task Generalization in Multi-Agent Reinforcement Learning
Oct 21, 2026Zijian Guo, İlker Işık, H. M. Sabbir Ahmad +1Multi-Agent Reinforcement Learning Multi-Agent Coordination
Division of Systems Engineering, Boston University · Department of Electrical and Computer Engineering, Boston University
MultiAgent-OPD: End-to-End Training of Hierarchical Multi-Agent Systems with On-Policy Distillation
Oct 16, 2026Yuhan Sun, Jiaxin Zhao, Yongdong Luo +6Multi-Agent LLM Systems LLM Agent Training
Alibaba Group · Zhejiang University
CHMAS: A Coupled Hierarchical Framework for Multi-Agent Reinforcement Learning
Jul 21, 2026Dongming Wang, Jie Xu, Yanyu Zhang +1Multi-Agent Coordination Asynchronous RL
Department of Electrical and Computer Engineering, University of California, Riverside, CA 92521, USA
Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems
Jul 3, 2026Hyunsoo Lee, Panggah Prabawa, Dae-Hyun Choi +1Energy-Efficient ML Data Center Energy Management
LG Electronics, 17709, Pyeongtaek, Gyeonggi-do, Republic of Korea · School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea · Department of Electrical and Computer Engineering, Korea University, Seoul 02841, Republic of Korea
Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control
Jun 22, 2026Zihao Guo, Jianing Zhao, Ling Li +3Hierarchical RL Multi-Agent Coordination
1King’s College London · University of California, Berkeley · 3The Alan Turing Institute
Hierarchical Control in Multi-Agent Games: LLM-based Planning and RL Execution
Jun 18, 2026Jannik Hösch, Alessandro Sestini, Florian Fuchs +6Multi-Agent Planning Multi-Agent Coordination
1Electronic Arts (EA) · 2KTH Royal Institute of Technology, Stockholm, Sweden
A Hierarchical MARL-Based Approach for Coordinated Retail P2P Trading and Wholesale Market Participation of DERs
Apr 22, 2026Patrick Wilk, Ethan Cantor, Yikui Liu +1Hierarchical MARL Multi-Agent Reinforcement Learning
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
Intelligent Three Level Learning Architecture for Autonomous UAV Swarms in Search and Rescue
Apr 9, 2026Oleksii BychkovHierarchical MARL Swarm Robotics
Department of Program Systems and Technologies, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
Hierarchical Lead Critic based Multi-Agent Reinforcement Learning
Feb 25, 2026David Eckel, Henri MeeßHierarchical MARL Multi-Agent Reinforcement Learning
Fraunhofer Institute IVI, Ingolstadt, Germany
Safe Multi-Agent Deep Reinforcement Learning for Privacy-Aware Edge-Device Collaborative DNN Inference
Feb 23, 2026Hong Wang, Xuwei Fan, Zhipeng Cheng +4Hierarchical MARL Constrained MARL
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
Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning
Jul 31, 2025Tommaso Marzi, Cesare Alippi, Andrea CiniMulti-Agent Coordination Hierarchical MARL
IDSIA USI-SUPSI, Università della Svizzera italiana, Lugano, Switzerland · Politecnico di Milano, Milan, Italy · EPFL, Lausanne, Switzerland
HiMA-Ecom: A Long-Horizon Memory Benchmark for Hierarchical Multi-Agent E-commerce Assistants
Jun 24, 2025Junxing Hu, Ai Han, Haolan Zhan +8Hierarchical MARL Long-Horizon Agent Evaluation
JD Retail, JD.com, Inc., Beijing, 101111, China · Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, Victoria, 3082, Australia · School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China

Nov 3, 2026J/K move · Enter open · S save

JHiMA-WebRL: A Joint Hierarchical Multi-Agent Reinforcement Learning Framework for Long-Horizon Web Tasks

Shanka Subhra Mondal, Jiayun Wang

Center for Advanced AI, Accenture

RL for GUI Agents Long-Horizon Agent Tasks Hierarchical MARL Group Relative Policy Optimization

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Multimodal web agents have shown promising capabilities for interacting with websites through graphical user interfaces (GUIs), yet realistic web tasks often require long-horizon, multi-step planning that remains challenging for monolithic policies. Such agents must jointly infer high-level plans, navigate through intermediate states, track progress over extended interaction histories, and execute visually grounded actions. Although prior work has explored modular multi-agent systems with a separate planner, the planner is typically not updated jointly with the actor. We investigate whether explicitly separating and jointly training planning and execution provides a stronger inductive bias for long-horizon web navigation. We introduce JHiMA-WebRL, a joint hierarchical multi-agent reinforcement learning framework for web agents that couples a task decomposer with an actor. To train the decomposer, we use a strong teacher model to construct structured subtask supervision from multimodal GUI trajectories, and use these annotations to fine-tune both the decomposer and the subtask-conditioned actor. We then extend Group Relative Policy Optimization (GRPO) to jointly optimize the decomposer and actor in the interactive WebArena environment through hierarchical rollout grouping and role-specific advantages. On WebArena-Lite-v2, JHiMA-WebRL-14B, consisting of a 7B actor and a 7B decomposer, achieves an average success rate of 37.2%, compared with 24% for a ScaleCUA-32B agent that we fine-tune with RL on the same training tasks and evaluate under the same protocol. Ablations show that jointly updating the decomposer during RL outperforms updating the actor alone. These results suggest that jointly learned role factorization with smaller models can be a competitive alternative to scaling monolithic agents for long-horizon web tasks.