探讨了在无线网络树型路由中寻找最优父节点的经典问题，分析了影响树型路由决策规则的多个指标，如接收信号强度的加权平均值、缓冲区占用率和功耗比。提出了一种基于强化学习增强树路由协议和强化学习算法在无线传感器网络中应用的系统模型，并详细说明了所提出的基于树的路由协议的基本操作，为循环检测父节点更新了算法；为了在复杂的场景中做出自适应决策，定义了一个状态空间、动作集和激励函数。通过试错找到激励最高的最佳父节点；并通过模拟比较研究，验证了父节点选择方案在性能指标（即端到端延迟、可靠性和能量消耗）之间进行合理权衡。
The classical problem of finding the optimal parent node in wireless network tree routing is discussed in this study. Various indexes affecting the decision rules of tree routing are analyzed, such as weighted average received signal strength, buffer occupation rate and power consumption ratio. A system model of enhanced tree routing protocol and reinforcement learning algorithm based on reinforcement learning is proposed in wireless sensor networks. The basic operation of the proposed tree-based routing protocol is described in detail, and the algorithm is updated for cyclic detection of parent node. In order to make adaptive decisions in complex scenarios, a state space, an action set and an excitation function are defined. The optimal parent node with the highest excitation is identified through trial and error. Through simulation and comparative study, it is verified that the parent node selection scheme achieves reasonable tradeoff among the performance indicators such as end-to-end delay, reliability and energy consumption. Through simulation and comparative analysis, the efficacy of the parent node selection scheme is validated, demonstrating a judicious tradeoff among performance indicators such as end-to-end delay, reliability, and energy consumption.
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