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The study of electromagnetic detection satellite scheduling problem (EDSSP)has attracted attention due to the detection requirements for a large number oftargets. This paper proposes a mixed-integer programming model for the EDSSPproblem and a genetic algorithm based on reinforcement learning (RL-GA).Numerous factors that affect electromagnetic detection are considered in themodel, such as detection mode, bandwidth, and other factors. The RL-GA embeds aQ-learning method into an improved genetic algorithm, and the evolution of eachindividual depends on the decision of the agent. Q-learning is used to guidethe population search process by choosing evolution operators. In this way, thesearch information can be effectively used by the reinforcement learningmethod. 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 toimprove 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 proposedalgorithm. Through the experimental verification of multiple instances, it canbe seen that the RL-GA can solve the EDSSP problem effectively. Compared withthe state-of-the-art algorithms, the RL-GA performs better in several aspects.

RL-GA: A Reinforcement Learning-Based Genetic Algorithm for Electromagnetic Detection Satellite Scheduling Problem