RLaGA: Reinforcement Learning-Assisted Genetic Algorithm for Satellite Resource Scheduling

Puntos clave

• The aim is to enhance satellite resource scheduling through a hybrid reinforcement learning and genetic algorithm approach.
• Developed a reinforcement learning-assisted genetic algorithm for resource allocation.
• Conducted simulations to test the algorithm on various scheduling scenarios.
• Evaluated performance metrics against traditional scheduling methods.
• The proposed algorithm showed improved efficiency in resource allocation.
• Significant reductions in scheduling conflicts were observed compared to baseline methods.
• Enhanced adaptability to varying satellite mission requirements.