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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 3.

The decoder comprises an LSTM module, two dense layers, and a learnable parameters set. The LSTM takes the outputs of the encoder as input to form a hybrid context to capture the sequence states. The outputs of the two dense layers are added, and the sum is sent to the learnable parameters block. After adding the result with the logarithmic mask, the softmax layer calculates the probability distribution of unscheduled targets.

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