标题： 基于Q学习的异构网络协作控制自适应重连 显示英文标题

标题： Q-Learning--Driven Adaptive Rewiring for Cooperative Control in Heterogeneous Networks

摘要： 多智能体系统中的合作出现代表了一个基本的统计物理问题，其中微观的学习规则驱动宏观集体行为的转变。 我们提出了一种基于Q学习的自适应重连变体，该变体建立在文献中研究的机制之上。 这种方法将时间差分学习与网络重构相结合，使代理可以根据交互历史优化策略和社会连接。 通过特定邻居的Q学习，代理发展出复杂的伙伴关系管理策略，能够形成合作者群集，在合作和缺陷区域之间创建空间分离。 使用反映现实世界异质连接模式的幂律网络，我们在不同重连约束水平下评估涌现行为，揭示了参数空间中的不同合作模式，而不是尖锐的热力学相变。 我们的系统分析确定了三种行为模式：一个宽松模式（低约束）促进快速合作群集形成，一个中间模式对困境强度敏感，以及一个耐心模式（高约束），在该模式下战略积累逐渐优化网络结构。 模拟结果表明，虽然适度的约束创造了抑制合作的过渡区域，但完全自适应的重连通过系统地探索有利的网络配置来增强合作水平。 定量分析显示，增加的重连频率推动大规模群集形成，并具有幂律大小分布。 我们的结果确立了一个新的范式，用于理解复杂自适应系统中的智能驱动合作模式形成，揭示了机器学习如何作为多智能体网络中自发组织的替代驱动力。 显示英文摘要

摘要： Cooperation emergence in multi-agent systems represents a fundamental statistical physics problem where microscopic learning rules drive macroscopic collective behavior transitions. We propose a Q-learning-based variant of adaptive rewiring that builds on mechanisms studied in the literature. This method combines temporal difference learning with network restructuring so that agents can optimize strategies and social connections based on interaction histories. Through neighbor-specific Q-learning, agents develop sophisticated partnership management strategies that enable cooperator cluster formation, creating spatial separation between cooperative and defective regions. Using power-law networks that reflect real-world heterogeneous connectivity patterns, we evaluate emergent behaviors under varying rewiring constraint levels, revealing distinct cooperation patterns across parameter space rather than sharp thermodynamic transitions. Our systematic analysis identifies three behavioral regimes: a permissive regime (low constraints) enabling rapid cooperative cluster formation, an intermediate regime with sensitive dependence on dilemma strength, and a patient regime (high constraints) where strategic accumulation gradually optimizes network structure. Simulation results show that while moderate constraints create transition-like zones that suppress cooperation, fully adaptive rewiring enhances cooperation levels through systematic exploration of favorable network configurations. Quantitative analysis reveals that increased rewiring frequency drives large-scale cluster formation with power-law size distributions. Our results establish a new paradigm for understanding intelligence-driven cooperation pattern formation in complex adaptive systems, revealing how machine learning serves as an alternative driving force for spontaneous organization in multi-agent networks.