分布式智能体之间的协作是众多复杂系统的基础，尤其在需在能量约束下维持连通性的通信网络中至关重要。本研究利用通过强化学习技术训练的智能体（节点），使其与邻近节点建立连接，最终自发形成大规模通信簇。值得注意的是，该系统不存在中心化管理者；智能体仅能依据局部观测信息自主调整连接关系。连接策略基于物理哈密顿量（Hamiltonian）构建，因此该智能系统属于“物理引导的机器学习”（Physics-Guided Machine Learning）范式。智能体采用深度Q网络（Deep Q-Network）进行训练，以局部观测为输入，以最小化哈密顿量变化为目标，从而实现在动态环境中的自适应决策。仿真结果表明，所提出的协作策略可构建鲁棒的大规模通信簇，并较基线方法降低传输能耗。该网络在智能体移动、节点密度变化、节点失效及环境障碍等条件下均保持高连通性，展现出优异的适应性与韧性。上述发现表明，物理引导的强化学习为新兴物联网（IoT）与车载通信网络中的分布式拓扑优化提供了一种有效机制。

Collaboration among distributed agents is fundamental to many complex systems, particularly in communication networks where connectivity must be maintained under energy constraints. In this study, we utilize intelligent agents (nodes) trained through reinforcement learning techniques to establish connections with their neighbors, ultimately leading to the emergence of a large-scale communication cluster. Notably, there is no centralized administrator; instead, agents must adjust their connections based on information obtained from local observations. The connection strategy is formulated using a physical Hamiltonian, thereby categorizing this intelligent system under the paradigm of "Physics-Guided Machine Learning". Agents are trained via a Deep Q-Network using local observations to minimize changes in the Hamiltonian, enabling adaptive decision-making in dynamic environments. Simulation results demonstrate that the proposed collaborative strategy forms robust large-scale communication clusters while reducing transmission energy compared to baseline approaches. The network maintains high connectivity under agent mobility, density variations, node failures, and environmental obstacles, highlighting strong adaptability and resilience. These findings indicate that physics-guided reinforcement learning provides an effective mechanism for distributed topology optimization in emerging IoT and vehicular communication networks.