Hierarchical Frameworks for Scaling-up Multi-agent Coordination

Minghong Geng

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 hierarchical approaches 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 for long-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 explores complex bimanual control tasks, specifically investigating multi-agent approaches for coordinated dual-hand manipulation.

External IDs:dblp:conf/ifaamas/Geng25