A Reliable Self-Organized Distributed Complex Network for Communication of Smart Agents
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.