Enhancing Remote-Sensing Satellite Task Scheduling Using Reinforcement Learning and Multi-Objective Optimization

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The integration of operational constraints improves practical outcomes in satellite task scheduling, emphasizing realism.
Results show that optimizing failure rates and mission timings enhances overall system performance for remote-sensing satellites.
Approach utilizes reinforcement learning and Pareto front strategies to address complex task scheduling challenges in dynamic environments.
The findings highlight the tradeoff between solution diversity and operational realism in satellite planning enhancements.

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Research reveals enhancing task scheduling for remote-sensing satellites using reinforcement learning and multi-objective optimization.

Khatir et al. (2025) studied this question.

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