Effective governance and steering of behavior in complex multi-agent systems (MAS) are essential for managing system-wide outcomes, particularly in environments where interactions are structured by dynamic networks. In many applications, the goal is to promote pro-social behavior among agents, where network structure plays a pivotal role in shaping these interactions. This paper introduces a Hierarchical Graph Reinforcement Learning (HGRL) framework that governs such systems through targeted interventions in the network structure. Operating within the constraints of limited managerial authority, the HGRL framework demonstrates superior performance across a range of environmental conditions, outperforming established baseline methods. Our findings highlight the critical influence of agent-to-agent learning (social learning) on system behavior: under low social learning, the HGRL manager preserves cooperation, forming robust core-periphery networks dominated by cooperators. In contrast, high social learning accelerates defection, leading to sparser, chain-like networks. Additionally, the study underscores the importance of the system manager's authority level in preventing system-wide failures, such as agent rebellion or collapse, positioning HGRL as a powerful tool for dynamic network-based governance.

翻译：有效治理和引导复杂多智能体系统中的行为对于管理系统层面的结果至关重要，尤其是在交互由动态网络结构化的环境中。在许多应用中，目标是促进智能体间的亲社会行为，而网络结构在塑造这些交互中起着关键作用。本文提出了一种分层图强化学习框架，该框架通过对网络结构进行针对性干预来治理此类系统。在管理权限有限的约束条件下，HGRL框架在一系列环境条件下均表现出卓越性能，超越了现有的基线方法。我们的研究结果凸显了智能体间学习对系统行为的关键影响：在低社会学习条件下，HGRL管理者能够维持合作，形成由合作者主导的稳健核心-边缘网络；反之，高社会学习会加速背叛行为，导致形成更稀疏的链式网络。此外，本研究强调了系统管理者权限水平在预防系统范围故障（如智能体叛乱或崩溃）中的重要性，从而将HGRL定位为基于动态网络的治理的有力工具。