CHMAS: A Coupled Hierarchical Framework for Multi-Agent Reinforcement Learning

2026-07-21

AI Summary

This work addresses the challenge of balancing global coordination and local execution in multi-agent reinforcement learning under asynchronous time scales. The authors propose a Coupled Hierarchical Multi-Agent System (CHMAS) that decomposes decision-making into centralized strategic planning and distributed tactical execution. A novel bidirectional feedback mechanism is introduced, wherein a coupling coefficient λ enables strategic objectives to dynamically adapt to tactical rewards, while an asynchronous update protocol mitigates environmental non-stationarity. The approach integrates a bilevel optimization framework, neighborhood-augmented distributed policies, and an analytically tractable additive approximation. Theoretical analysis establishes that the strategic layer achieves a convergence rate of 𝒪(log K/√K) after K updates. Empirical results demonstrate that the system converges stably and effectively learns spatially partitioned exploration strategies.

Abstract

Multi-agent reinforcement learning (MARL) systems face fundamental challenges in balancing global coordination with local execution across different temporal scales. This paper introduces the Coupled Hierarchical Multi-Agent System (CHMAS), a novel framework that decomposes multi-agent decision-making into centralized strategic planning and distributed tactical execution with bidirectional information flow. The strategic layer integrates all agents' states with an exclusive global environmental state to generate guidance actions every $T$ timesteps, while tactical agents execute distributed policies augmented by strategic guidance and local neighborhood observations. Unlike existing hierarchical approaches with unidirectional control, CHMAS establishes a feedback mechanism where accumulated tactical rewards influence strategic objectives through a coupling coefficient $λ$, ensuring strategic plans remain grounded in tactical feasibility. To address the non-stationarity inherent in hierarchical learning, we propose an asynchronous update protocol where strategic parameters update every $N_f$ tactical episodes, allowing tactical policies to converge to quasi-stationary points between strategic changes. We present both a general bi-level formulation capturing full system dynamics and a tractable additive approximation enabling rigorous analysis. Theoretical analysis proves that this asynchronous scheme achieves $\mathcal{O}(\log K/\sqrt{K})$ convergence for the strategic layer after $K$ strategic updates under standard assumptions. Experimental validation in a multi-agent foraging domain demonstrates successful learning of spatially partitioned exploration strategies, with both layers converging stably despite hierarchical coupling.

Problem

• multi-agent reinforcement learning
• hierarchical decision-making
• global coordination
• local execution
• non-stationarity

Innovation

• hierarchical multi-agent reinforcement learning
• bidirectional coupling
• asynchronous update
• non-stationarity mitigation
• strategic-tactical decomposition