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

arXiv: https://arxiv.org/abs/2206.05694 · HTML: https://mlsanity.com/paper/2206.05694/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 40 references that are in the map.
- First posted on arXiv 12 Jun 2022.

## Builds on (most cited first, 4 of 4)

- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions](https://mlsanity.com/paper/2003.06169/) (2020)
- [A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem](https://mlsanity.com/paper/2206.03185/) (2022)
- [A Mixed Integer Linear Programming Model for Multi-Satellite Scheduling](https://mlsanity.com/paper/1811.12114/) (2018)

## Built on by (most cited first, 8 of 8)

- [Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms](https://mlsanity.com/paper/2401.11963/) (2024)
- [Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities](https://mlsanity.com/paper/2308.13420/) (2023)
- [Evolutionary Reinforcement Learning: A Systematic Review and Future Directions](https://mlsanity.com/paper/2402.13296/) (2024)
- [Mathematical Models and Reinforcement Learning based Evolutionary Algorithm Framework for Satellite Scheduling Problem](https://mlsanity.com/paper/2301.02764/) (2023)
- [Dual-Individual Genetic Algorithm: A Dual-Individual Approach for Efficient Training of Multi-Layer Neural Networks](https://mlsanity.com/paper/2504.17346/) (2025)
- [Reinforcement Learning-Guided NSGA-II Enhanced with Gray Relational Coefficient for Multi-Objective Optimization: Application to NASDAQ Portfolio Optimization](https://mlsanity.com/paper/2607.16194/) (2026)
- [Task-Driven Three-Layer Distributed Scheduling for Emergency Earth Observation in Large Low-Earth-Orbit Constellations](https://mlsanity.com/paper/2608.14789/) (2026)
- [Implicit Q-learning-bootstrapped ant colony optimization for maritime moving-target observation scheduling with agile satellites](https://mlsanity.com/paper/2608.24471/) (2026)

## Abstract (arXiv)

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.