In this paper, we propose a dueling double deep Q-network (DDQN)-based adaptive multi-objective handover framework for LEO satellite networks. The proposed method enables dynamic trade-off learning among throughput, blocking probability, and switching cost under time-varying network conditions. Simulation results demonstrate that the proposed approach consistently outperforms conventional baselines, achieving up to 10.3% throughput improvement and near-zero blocking under typical operating conditions.

翻译：本文提出了一种基于决斗双深度Q网络（Dueling DDQN）的自适应多目标切换框架，用于低轨卫星网络。该方法能够在时变网络条件下，动态学习吞吐量、阻塞概率与切换成本之间的权衡。仿真结果表明，所提方法在典型运行条件下始终优于传统基线方案，实现了高达10.3%的吞吐量提升，并达到接近零的阻塞率。