用分层图强化学习调控复杂系统，提升合作与稳定性。
Adaptive Network Intervention for Complex Systems: A Hierarchical Graph Reinforcement Learning Approach
- 通过分层图强化学习动态干预网络结构，实现精准治理。
- 低社会学习下形成核心-外围合作网络，高学习则致网络稀疏化。
- 适用于资源有限但需稳定治理的多智能体系统，如社会网络或交通调度。
在动态网络结构的多智能体系统中，有效治理和引导行为对整体结果至关重要。本文提出分层图强化学习（HGRL）框架，通过针对性地干预网络结构来实现系统治理。在管理权限受限的情况下，该框架在多种环境条件下均优于现有基线方法。研究发现，个体间学习（社交学习）对系统行为有决定性影响：低社交学习时，管理者能维持合作，形成由合作者主导的核心-外围网络；而高社交学习则加速背叛行为，导致网络结构趋于稀疏且呈链状。此外，研究强调了管理者权威水平在防止系统崩溃或代理叛乱中的关键作用，表明HGRL是动态网络治理的有力工具。
原文摘要 · Abstract (English)
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.
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