基于Q-learning的分布式自适应拓扑稳定性算法

Q-Learning Based Distributed Adaptive Algorithm for Topological Stability

针对移动节点对网络拓扑稳定性的影响,提出了一种预测相邻节点稳定联接的自适应分布式强化学习算法。各节点采用强化学习和学习区间自适应划分相结合的方法,利用相邻节点间的接收信号强度信息对相邻节点间的联接状态进行判定,最终预测出能够保持稳定联接的邻居节点集。通过多种条件下随机游走模型仿真,结果表明预测准确度均保持在95%左右,验证了该算法的有效性和稳定性。

Aiming at the influence of mobile nodes on network topological stability,an adaptive distributed reinforcement learning algorithm is proposed to predict the stable connection of adjacent nodes.Each node uses the method of combining reinforcement learning with adaptive division of learning intervals,uses the received signal strength information between adjacent nodes to determine the connection state between adjacent nodes,and finally predicts the set of neighbor nodes that can maintain stable connection.The simulation results of random walk model under various conditions show that the prediction accuracy is about 95%,which verifies the effectiveness and stability of the algorithm.