Dynamic changes in reward values and retrieval accuracy over training iterations during deep reinforcement learning (DRL) training.

<p>Dynamic changes in reward values and retrieval accuracy over training iterations during deep reinforcement learning (DRL) training.</p>

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Bibliographic Details
Main Author:	Yali Chen (24494) (author)
Other Authors:	Bin Hu (122268) (author), Yajuan Liu (188669) (author)
Published:	2025
Subjects:	Sociology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified mean reciprocal rank experiencing explosive growth effectively handle multi aida corpus datasets multimodal transformer architecture entity linking accuracy accurately retrieve information model &# 8217 poor retrieval accuracy multimodal retrieval technology multimodal archival retrieval optimizing document management multimodal archival data extending application scenarios improving retrieval performance rl model provides retrieval accuracy multimodal data information management model performance rl model multimodal transformers optimizing strategies retrieval strategies baseline model rl achieves expanding data xlink "> visual analysis thereby promoting tail entities response time paper proposes modal ambiguity key challenge intelligent optimization icdar 2023 handling long generalization capabilities effective solution digital age comparison models clear advantages also laying 8 %, 4 %.
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