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This research aims to enhance the scheduling efficiency of satellite missions using a reinforcement learning approach.

June 6, 2026ElectronicsOpen Access

TopoAgent: A Constraint-Structured Reinforcement Learning Agent for Heterogeneous Satellite Mission Scheduling

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This research aims to enhance the scheduling efficiency of satellite missions using a reinforcement learning approach.
Developed TopoAgent, an online reinforcement-learning agent specialized for heterogeneous satellite mission scheduling.
Modeled requests as service-path decisions and incorporated a constraint manager for scheduling.
Evaluated TopoAgent in a high-fidelity simulation environment using various scheduling scenarios.
Achieved a 74.7% mission completion rate over five simulation seeds.
Attained a 75.5% high-priority mission completion ratio.

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Randomized trial investigates satellite mission scheduling efficiency using TopoAgent, suggesting improved capacity management.

Ren et al. (2026) studied this question.

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