Hierarchical Frameworks for Scaling-up Multi-agent Coordination

Multi-agent 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 decisionmaking 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 multiagent approaches for coordinated dual-hand manipulation.