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This is a Experimental Study study.

October 16, 2025Open Access

Automatic scheduling of satellite tracking tasks by means of policy-gradient reinforcement learning and transformer-based pointer networks

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The method reduces missed passes by up to 54.3%, compared to traditional greedy searches, and 29.5% better than dynamic programming.
Performance analysis shows the scheduling operates efficiently, with an end-to-end runtime of 366.2 seconds for 3,500 satellites over 48 hours.
Utilizing policy-gradient reinforcement learning, the approach adapts to changing priorities and maintains feasibility in task scheduling.
This new method offers a fully automated alternative to traditional satellite scheduling planners, minimizing manual intervention.

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This framework demonstrates improved scheduling performance in satellite ground management, suggesting efficiency gains in real-time applications.

Dobariya et al. (2025) studied this question.

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This framework demonstrates improved scheduling performance in satellite ground management, suggesting efficiency gains in real-time applications.