论文标题
RL-GA：用于电磁检测卫星调度问题的基于增强学习的遗传算法
RL-GA: A Reinforcement Learning-Based Genetic Algorithm for Electromagnetic Detection Satellite Scheduling Problem
论文作者
论文摘要
电磁检测卫星调度问题（EDSSP）的研究引起了人们对大量目标的检测要求的关注。本文提出了针对EDSSP问题的混合组件编程模型，以及基于增强学习（RL-GA）的遗传算法。在模型中考虑了影响电磁检测的许多因素，例如检测模式，带宽和其他因素。 RL-GA将Q学习方法嵌入了改进的遗传算法中，并且每个个体的演变都取决于药物的决策。 Q学习用于通过选择进化运算符来指导人口搜索过程。这样，搜索信息可以通过增强学习方法有效地使用。在算法中，我们设计一个奖励功能来更新Q值。根据问题特征，提出了<state，Action>的新组合。 RL-GA还使用精英个人保留策略来提高搜索性能。之后，提出了任务时间窗口选择算法（TTWSA）来评估人口进化的性能。几个实验用于检查所提出算法的调度效果。通过对多个实例的实验验证，可以看出RL-GA可以有效地解决EDSSP问题。与最先进的算法相比，RL-GA在几个方面的表现更好。
The study of electromagnetic detection satellite scheduling problem (EDSSP) has attracted attention due to the detection requirements for a large number of targets. This paper proposes a mixed-integer programming model for the EDSSP problem and a genetic algorithm based on reinforcement learning (RL-GA). Numerous factors that affect electromagnetic detection are considered in the model, such as detection mode, bandwidth, and other factors. The RL-GA embeds a Q-learning method into an improved genetic algorithm, and the evolution of each individual depends on the decision of the agent. Q-learning is used to guide the population search process by choosing evolution operators. In this way, the search information can be effectively used by the reinforcement learning method. In the algorithm, we design a reward function to update the Q value. According to the problem characteristics, a new combination of <state, action> is proposed. The RL-GA also uses an elite individual retention strategy to improve search performance. After that, a task time window selection algorithm (TTWSA) is proposed to evaluate the performance of population evolution. Several experiments are used to examine the scheduling effect of the proposed algorithm. Through the experimental verification of multiple instances, it can be seen that the RL-GA can solve the EDSSP problem effectively. Compared with the state-of-the-art algorithms, the RL-GA performs better in several aspects.
