Task Scheduling for Single Satellite Observation Based on Meta-Reinforcement Learning

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Meta-reinforcement learning improves task scheduling efficiency for satellite observation tasks, maximizing rewards.
The model leverages a Markov decision process for the 0-1 knapsack problem to optimize task completion.
The comparative analysis shows the meta-RL algorithm outperforms traditional deep reinforcement learning methods.
Effective in handling large-scale scheduling, demonstrating enhanced generalization across different task sets.

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Algorithm demonstrates improved task scheduling efficiency in satellite operations, indicating potential for large-scale planning.

Li et al. (2025) studied this question.

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