Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2025

Abstract

Multi-agent reinforcement learning has emerged as a powerful framework for developing collaborative behaviors in autonomous systems. However, existing MARL methods often struggle with scalability in terms of both the number of agents and decision-making horizons. My research focuses on developing hierarchicalapproaches to scale up MARL systems through two complementary directions: structural scaling by increasing the number of coordinated agents and temporal scaling by extending planning horizons. My initial work introduced HiSOMA, a hierarchical framework integrating self-organizing neural networks with MARL forlong-horizon planning, and MOSMAC, a benchmark for evaluating MARL methods on multi-objective MARL scenarios. Building on these foundations, my recent work studies L2M2, a novel framework that leverages large language models for high-level planning in hierarchical multi-agent systems. My ongoing research explorescomplex bimanual control tasks, specifically investigating multi-agent approaches for coordinated dual-hand manipulation.

Keywords

Multi-agent Reinforcement Learning, Hierarchical Multi-agent System, Large Language Model, Benchmark

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

AAMAS 2025: Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, May 19-23, Michigan

First Page

2932

Last Page

2934

ISBN

9798400714269

Publisher

International Foundation for Autonomous Agents and Multiagent Systems

City or Country

Richland, SC

Additional URL

https://www.ifaamas.org/Proceedings/aamas2025/pdfs/p2932.pdf

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