Learning Selective Merge Policies for Deadline-Constrained Coded Caching via Deep Reinforcement Learning
A new paper presents a deep reinforcement learning approach to optimize coded caching for deadline-sensitive applications. The proposed method focuses on selective merging of messages to improve efficiency while reducing broadcast-packet expiration. Results show a significant reduction in expiration rates compared to existing methods, highlighting the effectiveness of selective merging in meeting tight deadlines.
- ▪The study addresses the challenge of merging messages in coded caching for users with strict deadlines.
- ▪A graph-attention policy network was trained using proximal policy optimization to enhance performance.
- ▪The proposed method reduces the broadcast-packet expiration ratio by 40.9% compared to the best existing baseline.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15236 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Information Theory arXiv:2605.15236 (cs) [Submitted on 13 May 2026] Title:Learning Selective Merge Policies for Deadline-Constrained Coded Caching via Deep Reinforcement Learning Authors:Amirhossein Yousefiramandi View a PDF of the paper titled Learning Selective Merge Policies for Deadline-Constrained Coded Caching via Deep Reinforcement Learning, by Amirhossein Yousefiramandi View PDF HTML (experimental) Abstract:With the coded caching, the server can use the information the users have cached to serve multiple users at a time by sending a single coded multi-casting message, i.e., the merged message, thereby relieving the peak network loads.
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