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Deep Reinforcement Learning for Efficient Scheduling of Ground-based Astronomical Observations

  • Authors: Hai Cao, Shaoming Hu, Junju Du, Xu Chen, Shuqi Liu, Shuai Feng, Bo Zhang, Yuchen Jiang

Hai Cao et al 2025 The Astronomical Journal 170 .

  • Provider: AAS Journals

Caption: Figure 8.

Dual-telescope observation plan execution: DRL-GR results outperform the greedy and random scheduling. Sequential processes are marked by red arrows. Parenthetical values denote the total theoretical reward scores. DRL-GR-1 plans (bottom left) achieve 33.9% higher task completion and 24.1% reward gain despite wider spatial distribution vs. the greedy benchmark.

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